Arrangements of documents in a document feed

ABSTRACT

Some embodiments provide a GUI for a document reader application that displays an aggregated feed with sections for different groups of personalized documents. Some embodiments provide a method for arranging documents within the different sections and for ordering the different sections within the aggregated feed. In some embodiments, the groups are dynamically generated at a device based on content (e.g., tags) of the documents.

BACKGROUND

Document readers are often used for viewing electronic documents, suchas digital articles or webpages, on a device. The digital articles areoften personalized (e.g., selected based on user preferences) fromvarious sources or publishers, and related to a variety of differenttopics. Some document readers provide an aggregated feed that displayssummaries for the assortment of personalized documents for viewing bythe user. However, an unorganized display of such a wide variety ofdifferent articles can be difficult to navigate and to read.

BRIEF SUMMARY

Some embodiments provide a document reader application (also referred toas a document viewing application) for viewing and navigating betweendocuments from a variety of different sources and related to a varietyof different topics. The document reader application of some embodimentscollects documents (e.g., magazine articles, web blog posts, wordprocessing documents, slides of a presentation, etc.) from the varietyof different sources (e.g., websites, magazine publishers, newspapers,etc.).

The document reader application of some embodiments includes a graphicaluser interface (GUI) that presents document panes (e.g., visualsummaries) for documents of an aggregated feed in a display area of adevice. The aggregated feed includes common documents (e.g., curatedcontent, advertisements, trending stories, curated topics, etc.), whichare not selected based on preferences of a user, and personalizeddocuments, which are selected based on user preferences (e.g., favoritesources, favorite topics, reading habits, etc.).

In some embodiments, the personalized documents are received from aserver and dynamically grouped into various document groups at thedevice on which the document reader application executes. The dynamicgrouping of some embodiments is performed based on a topic associatedwith each group. The topics of some embodiments are identified based onan analysis of the received personalized documents (e.g., through ananalysis of tags associated with the documents).

The GUI of some embodiments displays the common and personalizeddocuments in various sections of the aggregated feed. In someembodiments, the GUI includes at least one common section that displaysdocument panes for common documents and several group sections for thepersonalized documents. Each group section displays a set of documentpanes for a corresponding group of personalized documents. The groupsections of some embodiments display various controls for interactingwith the group. For example, some embodiments of the group sectionsprovide a user interface (UI) control for displaying additionaldocuments related to a corresponding topic associated with a particularsection. In some embodiments, the GUI also provides a UI control foradding the corresponding topic to a list of favorite topics associatedwith the user. In some embodiments, the GUI also displays an ungroupedsection that displays document panes for a set of personalized documentsthat are not grouped with any other personalized documents. Theaggregated feed of some embodiments intersperses common sections betweenthe group sections. The common sections of some embodiments aredisplayed with a different appearance (e.g., color, format, etc.) fromthat of the group sections.

The GUI of some embodiments also displays at least one featured sectionfor displaying document panes for a set of featured documents. Thefeatured documents of some embodiments include premium content specifiedby the publishers of the content, content that requires a subscription,curated and high-quality content, etc. In some embodiments, the featuredsection displays (or emphasizes) a single featured document at a time,and displays the documents by animating transitions between documentpanes for the featured documents. In some embodiments, the document panefor at least one featured document includes a multi-layer image, andanimating the transition creates a parallax effect using the differentlayers of the multi-layer image. In some embodiments, the document panesfor the featured documents are based on images associated with thefeatured document and sized according to various aspect ratios (e.g.,3:4, 4:3, 1:1, etc.) of the associated image.

In some embodiments, the GUI arranges group sections along a firstdimension of the display area of the device, while displaying documentpanes for featured documents of the featured section along a different,second dimension in the display area. For example, in some embodiments,the first dimension is a vertical dimension and the second dimension isa horizontal dimension.

In some embodiments, the GUI initially presents a set of common sectionsfor display while performing the dynamic grouping of the personalizeddocuments. The group sections for the grouped personalized documents arethen displayed upon receiving input (e.g., through a swipe motion) tonavigate through the documents of the aggregated feed.

The document reader application of some embodiments will providedifferent groups for the personalized documents depending on the amountof time before the input is received. In some embodiments, when theinput is received before an acceptable grouping is completed, a subsetof the personalized documents are presented in an ungrouped layout,providing the user with personalized content to review, while stillattempting to dynamically generate group sections for the remainingpersonalized documents.

Some embodiments provide a method for arranging and displaying documentpanes for documents in sections of a display layout. The methodidentifies several dynamically grouped document groups, and, for eachdocument group, arranges document panes for the documents of thedocument group in a section of the display layout. Each section, in someembodiments, includes a single head subsection and zero or more bodysubsections. The head subsection of some embodiments displays a primarydocument pane for a primary document. In some embodiments, the primarydocument pane is the largest document pane in the section and isdisplayed with a special color scheme that is different from colorschemes of the secondary document panes. The primary document pane ofsome embodiments is the only document pane to display excerpt (orsummary) text.

In some embodiments, the method arranges document panes using a set ofstatic templates. The method of some embodiments selects the templatesfrom different sets of templates, based on a size or resolution of thedisplay area. The static templates of some embodiments include a set ofhead templates for a head subsection and a set of body templates forbody subsections. Some of the head templates further display secondarydocument panes along with the primary document pane. In someembodiments, a head template can span two primary document panes. Thetemplates of some embodiments are selected based on various criteria(e.g., number of documents in the document group, transitions (oralignment) of portions of the different templates, etc.).

In some embodiments, the primary document for the primary document paneis identified based on a set of relevance scores, but the remainingdocuments are positioned within the templates of the head and bodysubsections based on visual scores calculated for the documents of thegroup. The visual score of some embodiments is calculated based onelements of the document pane (e.g., whether it has an image, a short orlong headline, etc.).

In some embodiments, the method generates multiple layouts for a sectionand determines whether to use a layout based on a set of scorescalculated for the section. In some embodiments, the method evaluatesthe layout of each section (e.g., determining how well the differentelements fit together). Alternatively, or conjunctively, the methodevaluates the layout of each section relative to the surroundingsections. For example, the method of some embodiments provides a lowerscore for a section layout that reuses a particular head template inconsecutive sections. In some embodiments, the method generates themultiple layouts for a specified period of time, or a specified numberof times, and then selects the highest scoring layout. The method ofsome embodiments performs a single-pass selection method that evaluatesthe section layout as it is generated, and does not generate multipledifferent layouts for a single section.

As mentioned, some embodiments display documents that are dynamicallyarranged in groups below the common content (e.g., top stories, trendingcontent, etc.). In some embodiments, the device receives a number ofdocuments (e.g., new documents since the last time the user has viewedthe document feed) and dynamically arranges these documents into groups.The documents as received are assigned one or more tags that eachindicate relevancy to a topic, and these tags are used for the groups insome embodiments. Of all the possible tags that are assigned to thereceived documents, the device identifies some or all of the tags foruse in grouping the documents. The device then dynamically assigns thedocuments to groups for at least a subset of these identified groupingtags. In some embodiments, this dynamic grouping involves iterativelyselecting tags and assigning some or all of the documents assigned thattag to a group for the tag, then removing the assigned documents for thenext iteration. With the documents assigned to groups, the device addsthe groups of documents to the feed (e.g., below the trending topics).In some embodiments, documents that are not assigned to any group aredisplayed below the groups in the feed.

In some embodiments, the device receives a set of documents that are newsince the last time the feed was accessed. This may be a large number ofdocuments, and the device then selects a subset of these documentsaccording to a personalization algorithm in some embodiments (thatpersonalizes the feed according to past history and/or specifiedpreferences of the user of the device). Some embodiments select up to afixed number of documents using the personalization algorithm (e.g., 25,60, 100, etc.).

Each of the selected documents (and the unselected documents, for thatmatter) are assigned zero or more tags, which in some embodimentsidentify that the document relates to a particular topic. In someembodiments, the tag assignment is performed by the servers thatamalgamate the documents and distribute the documents to the userdevices. In general, documents will have more than one tag, as the tagsmay range from specific (e.g., a specific person) to general (ahigh-level topic such as politics, sports, etc.). Of all the tags thatare assigned to at least one document, some embodiments identify a setof the tags to use for grouping (referred to as groupable tags) based ona set of criteria. For instance, some embodiments require that the tagbe assigned to a minimum number of documents to form an aesthetic groupin the feed (e.g., 3, 5, etc.). Alternatively, or in addition, someembodiments select tags from a “whitelist” (i.e., a set of tags definedfor use in grouping). This whitelist may be predefined in the code ofthe device, or updated by the set of servers as different topics becomemore popular within the articles provided by the set of servers. In someembodiments, the whitelist primarily contains more general tags forhigh-level topics in favor of very specific tags.

With the groupable tags identified, the device dynamically assigns thedocuments to groups. In some embodiments, the device iteratively selectsa tag to use to form a group, identifies one or more potential groups ofdocuments for the tag (out of all of the documents that are assigned thetag), uses a set of heuristics to select one of the identified potentialgroups, and assigns the documents in the selected potential group to agroup for the selected tag. The device then removes the documents in theselected group for the next iteration. In some embodiments, the goal ofthe iterative process is to minimize the number of documents that areleft unassigned to any group. In assigning documents to groups, someembodiments ensure that each group has at least a minimum number ofdocuments (e.g., 3, 5, etc.) and no more than a maximum number ofdocuments (e.g., 10, 12, 20, etc.), in order to form aestheticgroupings.

One aspect of this iterative process is that the selection of documentsfor a tag in a first iteration will affect which tag is selected in thenext iteration (and in subsequent iterations). That is, in some cases,selecting a first group of documents for a first tag results in thedevice selecting a second tag in the next iteration of the process,whereas selecting a second group of documents for the first tag resultsin the device selecting a third tag in the next iteration of the processrather than the second tag. This result occurs because documents may beassigned multiple groupable tags, and assigning a document to a groupfor a first one of its tags means that it will not be assigned to agroup for any of its other tags.

Different embodiments may use different criteria at various decisionpoints in the process. For instance, to determine which tag should beused for a group at a particular iteration of the process, someembodiments use as criteria the number of documents for which the tag isthe only assigned tag (referred to as “solos”) and the number ofdocuments overall that are assigned the tag. (e.g., preferring tags witha larger numbers of solos, using overall size for the tag as atiebreaker). Further ties may be broken by a random selection in someembodiments.

Once a tag is chosen for a particular iteration, some embodimentsidentify a set of potential groupings of the documents for the tag, witheach potential group including all of the solos for the group (unlessthe number of solos is larger than the maximum number in a group). Foreach potential group, the process calculates a short-term impact score,such as the number of documents that would remain that no longer haveany tags for which there are the minimum number of documents for a group(referred to as “orphans”). For example (assuming a minimum number ofthree documents per group), if three documents are assigned tag A, butone of these documents is then assigned to a group for tag B, this willleave only two remaining documents for tag A. If these documents are notassigned tags for any other groups, then at this point they will not beable to be assigned to a valid group, and are thus orphans. Asmentioned, some embodiments calculate an initial score for eachpotential group that identifies the short-term impact in terms of thenumber of orphans created by the group.

Some embodiments then select the top potential group for the iterationbased on this short-term impact evaluation (e.g., choosing one of thepotential groups randomly if there is a tie), and commit that group(thereby removing the selected documents for the next iteration). Someembodiments, however, identify more than one of the top potential groupsbased on the short-term impact, and compute a longer-term impactheuristic for each of these groups. For the longer-term impactheuristic, some embodiments use a greedy algorithm that approximates therest of the groupings and identifies a number of orphans that result. Ofthese potential groups, the process selects the group with the bestlonger-term heuristic score (again choosing randomly in case of a tie)and commits the group.

Because the processing may be time-constrained (the groups need to bedetermined before the user scrolls past the initial static content),some embodiments initially use only the short-term impact evaluationwhen iterating through the grouping process. If additional time isavailable, the process re-evaluates the entire set of groups byidentifying more potential groups for longer-term impact evaluation ateach iteration, in order to identify groups that leave fewer of thedocuments unassigned. Some embodiments continue to increase the numberof potential groups for which the longer-term impact is calculated untiltime runs out (because the user has scrolled past the static content) oruntil a pre-set maximum number of evaluations is reached.

The preceding Summary is intended to serve as a brief introduction tosome embodiments of the invention. It is not meant to be an introductionor overview of all inventive subject matter disclosed in this document.The Detailed Description that follows and the Drawings that are referredto in the Detailed Description further describe the embodimentsdescribed in the Summary as well as other embodiments. Accordingly, tounderstand all the embodiments described by this document, a full reviewof the Summary, the Detailed Description, and the Drawings is needed.Moreover, the claimed subject matters are not to be limited by theillustrative details in the Summary, the Detailed Description, and theDrawings, but rather are to be defined by the appended claims, becausethe claimed subject matters can be embodied in other specific formswithout departing from the spirit of the subject matters.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appendedclaims. However, for purposes of explanation, several embodiments of theinvention are set forth in the following figures.

FIG. 1 illustrates an example of a GUI for a document reader applicationthat displays sections of common content.

FIG. 2 illustrates an example of a GUI for a document reader applicationthat displays sections of personalized and curated content.

FIG. 3 illustrates an example of a featured article section.

FIG. 4 illustrates examples of different formats for document panes.

FIG. 5 illustrates examples of different head layout templates.

FIG. 6 illustrates examples of different body layout templates.

FIG. 7 conceptually illustrates a process for generating a layout withgroup sections for an aggregated feed.

FIG. 8 conceptually illustrates a process for ranking document contentbased on visual aesthetics.

FIG. 9 illustrates an example of providing UI tools for markingdocuments in a two-pane layout.

FIG. 10 illustrates another example of providing UI tools for markingdocuments in a two-pane layout.

FIG. 11 illustrates an example of providing an initial ungrouped sectionof personalized content.

FIG. 12 illustrates an example of providing grouped personalizedcontent.

FIG. 13 illustrates an example of generating different group layouts tominimize orphans.

FIG. 14 conceptually illustrates a state diagram for a document readerapplication that provides grouped personalized content.

FIG. 15 conceptually illustrates a set of documents along with multiplepossible groupings for those documents.

FIGS. 16A-B conceptually illustrates a process of some embodiments forperforming dynamic grouping operations.

FIG. 17 conceptually illustrates a calculation for the set of documentsfrom FIG. 15 that identifies statistics regarding several groupabletags.

FIG. 18 illustrates an example of the computation of potential groupsfor some of the documents from FIG. 15.

FIGS. 19-21 illustrate a representation of group selection as a tree forincreasing values of k, the branching factor.

FIG. 22 illustrates an overall process that a document reader of someembodiments performs on some embodiments to display a document feed pagethat displays summaries of several documents

FIG. 23 illustrates a process that identifies the hierarchicalrelationships between the identified groups and to define a presentationorder for the identified groups based on these relationships.

FIGS. 24A-C pictorially illustrate examples of several of the operationsof the process of FIG. 23.

FIGS. 25 and 26 illustrate examples of feed layout pages generatedaccording to the grouping order of the example of FIGS. 24A-C.

FIG. 27 illustrates a process that constructs a graph for a defined setof group nodes by assessing the parent-child relationships between thedefined nodes based on the usage of the group topic identifiers by allthe retrieved documents.

FIG. 28 illustrates a Venn diagram that illustrates the variables usedin the computation of relatedness metrics of some embodiments.

FIGS. 29 and 30 present two examples that illustrate some embodimentsdynamically define parent-child relationships between group topicidentifiers differently at different times based on different data thatreflects the news events in different news cycles.

FIG. 31 illustrates an example of an architecture of a mobile computingdevice with which some embodiments are implemented.

FIG. 32 conceptually illustrates another example of an electronic systemwith which some embodiments of the invention are implemented.

DETAILED DESCRIPTION

In the following detailed description of the invention, numerousdetails, examples, and embodiments of the invention are set forth anddescribed. However, it will be clear and apparent to one skilled in theart that the invention is not limited to the embodiments set forth andthat the invention may be practiced without some of the specific detailsand examples discussed.

Some embodiments provide a document reader application (also referred toas a document viewing application) for viewing and navigating betweendocuments from a variety of different sources and related to a varietyof different topics. The document reader application of some embodimentscollects documents (e.g., magazine articles, web blog posts, wordprocessing documents, slides of a presentation, etc.) from the varietyof different sources (e.g., websites, magazine publishers, newspapers,etc.).

The document reader application of some embodiments includes a graphicaluser interface (GUI) that presents document panes (e.g., visualsummaries) for documents of an aggregated feed in a display area of adevice. The aggregated feed includes common documents (e.g., curatedcontent, advertisements, trending stories, curated topics, etc.), whichare not selected based on preferences of a user, and personalizeddocuments, which are selected based on user preferences (e.g., favoritesources, favorite topics, reading habits, etc.).

In some embodiments, the personalized documents are received from aserver and dynamically grouped into various document groups at thedevice on which the document reader application executes. The dynamicgrouping of some embodiments is performed based on a topic associatedwith each group. The topics of some embodiments are identified based onan analysis of the received personalized documents (e.g., through ananalysis of tags associated with the documents).

The GUI of some embodiments displays the common and personalizeddocuments in various sections of the aggregated feed. In someembodiments, the GUI includes at least one common section that displaysdocument panes for common documents and several group sections for thepersonalized documents. Each group section displays a set of documentpanes for a corresponding group of personalized documents. The groupsections of some embodiments display various controls for interactingwith the group. For example, some embodiments of the group sectionsprovide a user interface (UI) control for displaying additionaldocuments related to a corresponding topic associated with a particularsection. In some embodiments, the GUI also provides a UI control foradding the corresponding topic to a list of favorite topics associatedwith the user. In some embodiments, the GUI also displays an ungroupedsection that displays document panes for a set of personalized documentsthat are not grouped with any other personalized documents. Theaggregated feed of some embodiments intersperses common sections betweenthe group sections. The common sections of some embodiments aredisplayed with a different appearance (e.g., color, format, etc.) fromthat of the group sections.

The GUI of some embodiments also displays at least one featured sectionfor displaying document panes for a set of featured documents. Thefeatured documents of some embodiments include premium content specifiedby the publishers of the content, content that requires a subscription,curated and high-quality content, etc. In some embodiments, the featuredsection displays (or emphasizes) a single featured document at a time,and displays the documents by animating transitions between documentpanes for the featured documents. In some embodiments, the document panefor at least one featured document include a multi-layer image andanimating the transition creates a parallax effect using the differentlayers of the multi-layer image. In some embodiments, the document panesfor the featured documents are based on images associated with thefeatured document and sized according to various aspect ratios (e.g.,3:4, 4:3, 1:1, etc.) of the associated image.

In some embodiments, the GUI arranges group sections along a firstdimension of the display area of the device, while displaying documentpanes for featured documents of the featured section along a different,second dimension in the display area. For example, in some embodiments,the first dimension is a vertical dimension and the second dimension isa horizontal dimension.

In some embodiments, the GUI initially presents a set of common sectionsfor display while performing the dynamic grouping of the personalizeddocuments. The group sections for the grouped personalized documents arethen displayed upon receiving input (e.g., through a swipe motion) tonavigate through the documents of the aggregated feed.

The document reader application of some embodiments will providedifferent groups for the personalized documents depending on the amountof time before the input is received. In some embodiments, when theinput is received before an acceptable grouping is completed, a subsetof the personalized documents are presented in an ungrouped layout,providing the user with personalized content to review, while stillattempting to dynamically generate group sections for the remainingpersonalized documents.

Some embodiments provide a method for arranging and displaying documentpanes for documents in sections of a display layout. The methodidentifies several dynamically grouped document groups, and, for eachdocument group, arranges document panes for the documents of thedocument group in a section of the display layout. Each section, in someembodiments, includes a single head subsection and zero or more bodysubsections. The head subsection of some embodiments displays a primarydocument pane for a primary document. In some embodiments, the primarydocument pane is the largest document pane in the section and isdisplayed with a special color scheme that is different from colorschemes of the secondary document panes. The primary document pane ofsome embodiments is the only document pane to display excerpt (orsummary) text.

In some embodiments, the method arranges document panes using a set ofstatic templates. The method of some embodiments selects the templatesfrom different sets of templates, based on a size or resolution of thedisplay area. The static templates of some embodiments include a set ofhead templates for a head subsection and a set of body templates forbody subsections. Some of the head templates further display secondarydocument panes along with the primary document pane. In someembodiments, a head template can span two primary document panes. Thetemplates of some embodiments are selected based on various criteria(e.g., number of documents in the document group, transitions (oralignment) of portions of the different templates, etc.).

In some embodiments, the primary document for the primary document paneis identified based on a set of relevance scores, but the remainingdocuments are positioned within the templates of the head and bodysubsections based on visual scores calculated for the documents of thegroup. The visual score of some embodiments is calculated based onelements of the document pane (e.g., whether it has an image, a short orlong headline, etc.).

In some embodiments, the method generates multiple layouts for a sectionand determines whether to use a layout based on a set of scorescalculated for the section. In some embodiments, the method evaluatesthe layout of each section (e.g., determining how well the differentelements fit together). Alternatively, or conjunctively, the methodevaluates the layout of each section relative to the surroundingsections. For example, the method of some embodiments provides a lowerscore for a section layout that reuses a particular head template inconsecutive sections. In some embodiments, the method generates themultiple layouts for a specified period of time, or a specified numberof times, and then selects the highest scoring layout. The method ofsome embodiments performs a single-pass selection method that evaluatesthe section layout as it is generated, and does not generate multipledifferent layouts for a single section.

As mentioned, some embodiments display documents that are dynamicallyarranged in groups below the common content (e.g., top stories, trendingcontent, etc.). In some embodiments, the device receives a number ofdocuments (e.g., new documents since the last time the user has viewedthe document feed) and dynamically arranges these documents into groups.The documents as received are assigned one or more tags that eachindicate relevancy to a topic, and these tags are used for the groups insome embodiments. Of all the possible tags that are assigned to thereceived documents, the device identifies some or all of the tags foruse in grouping the documents. The device then dynamically assigns thedocuments to groups for at least a subset of these identified groupingtags. In some embodiments, this dynamic grouping involves iterativelyselecting tags and assigning some or all of the documents assigned thattag to a group for the tag, then removing the assigned documents for thenext iteration. With the documents assigned to groups, the device addsthe groups of documents to the feed (e.g., below the trending topics).In some embodiments, documents that are not assigned to any group aredisplayed below the groups in the feed.

In some embodiments, the device receives a set of documents that are newsince the last time the feed was accessed. This may be a large number ofdocuments, and the device then selects a subset of these documentsaccording to a personalization algorithm in some embodiments (thatpersonalizes the feed according to past history and/or specifiedpreferences of the user of the device). Some embodiments select up to afixed number of documents using the personalization algorithm (e.g., 25,60, 100, etc.).

Each of the selected documents (and the unselected documents, for thatmatter) are assigned zero or more tags, which in some embodimentsidentify that the document relates to a particular topic. In someembodiments, the tag assignment is performed by the servers thatamalgamate the documents and distribute the documents to the userdevices. In general, documents will have more than one tag, as the tagsmay range from specific (e.g., a specific person) to general (ahigh-level topic such as politics, sports, etc.). Of all the tags thatare assigned to at least one document, some embodiments identify a setof the tags to use for grouping (referred to as groupable tags) based ona set of criteria. For instance, some embodiments require that the tagbe assigned to a minimum number of documents to form an aesthetic groupin the feed (e.g., 3, 5, etc.). Alternatively, or in addition, someembodiments select tags from a “whitelist” (i.e., a set of tags definedfor use in grouping). This whitelist may be predefined in the code ofthe device, or updated by the set of servers as different topics becomemore popular within the articles provided by the set of servers. In someembodiments, the whitelist primarily contains more general tags forhigh-level topics in favor of very specific tags.

With the groupable tags identified, the device dynamically assigns thedocuments to groups. In some embodiments, the device iteratively selectsa tag to use to form a group, identifies one or more potential groups ofdocuments for the tag (out of all of the documents that are assigned thetag), uses a set of heuristics to select one of the identified potentialgroups, and assigns the documents in the selected potential group to agroup for the selected tag. The device then removes the documents in theselected group for the next iteration. In some embodiments, the goal ofthe iterative process is to minimize the number of documents that areleft unassigned to any group. In assigning documents to groups, someembodiments ensure that each group has at least a minimum number ofdocuments (e.g., 3, 5, etc.) and no more than a maximum number ofdocuments (e.g., 10, 12, 20, etc.), in order to form aestheticgroupings.

One aspect of this iterative process is that the selection of documentsfor a tag in a first iteration will affect which tag is selected in thenext iteration (and in subsequent iterations). That is, in some cases,selecting a first group of documents for a first tag results in thedevice selecting a second tag in the next iteration of the process,whereas selecting a second group of documents for the first tag resultsin the device selecting a third tag in the next iteration of the processrather than the second tag. This result occurs because documents may beassigned multiple groupable tags, and assigning a document to a groupfor a first one of its tags means that it will not be assigned to agroup for any of its other tags.

Different embodiments may use different criteria at various decisionpoints in the process. For instance, to determine which tag should beused for a group at a particular iteration of the process, someembodiments use as criteria the number of documents for which the tag isthe only assigned tag (referred to as “solos”) and the number ofdocuments overall that are assigned the tag. (e.g., preferring tags witha larger numbers of solos, using overall size for the tag as atiebreaker). Further ties may be broken by a random selection in someembodiments.

Once a tag is chosen for a particular iteration, some embodimentsidentify a set of potential groupings of the documents for the tag, witheach potential group including all of the solos for the group (unlessthe number of solos is larger than the maximum number in a group). Foreach potential group, the process calculates a short-term impact score,such as the number of documents that would remain that no longer haveany tags for which there are the minimum number of documents for a group(referred to as “orphans”). For example (assuming a minimum number ofthree documents per group), if three documents are assigned tag A, butone of these documents is then assigned to a group for tag B, this willleave only two remaining documents for tag A. If these documents are notassigned tags for any other groups, then at this point they will not beable to be assigned to a valid group, and are thus orphans. Asmentioned, some embodiments calculate an initial score for eachpotential group that identifies the short-term impact in terms of thenumber of orphans created by the group.

Some embodiments then select the top potential group for the iterationbased on this short-term impact evaluation (e.g., choosing one of thepotential groups randomly if there is a tie), and commit that group(thereby removing the selected documents for the next iteration). Someembodiments, however, identify more than one of the top potential groupsbased on the short-term impact, and compute a longer-term impactheuristic for each of these groups. For the longer-term impactheuristic, some embodiments use a greedy algorithm that approximates therest of the groupings and identifies a number of orphans that result. Ofthese potential groups, the process selects the group with the bestlonger-term heuristic score (again choosing randomly in case of a tie)and commits the group.

Because the processing may be time-constrained (the groups need to bedetermined before the user scrolls past the initial static content),some embodiments initially use only the short-term impact evaluationwhen iterating through the grouping process. If additional time isavailable, the process re-evaluates the entire set of groups byidentifying more potential groups for longer-term impact evaluation ateach iteration, in order to identify groups that leave fewer of thedocuments unassigned. Some embodiments continue to increase the numberof potential groups for which the longer-term impact is calculated untiltime runs out (because the user has scrolled past the static content) oruntil a pre-set maximum number of evaluations is reached.

Many examples of dynamic grouping and a document reader application thatdisplays personalized content are described below. Section I describes aGUI for a document reader application that displays sections fordocument groups. Section II then describes a grouping algorithm used forgrouping documents into groups for the sectioned display. Section IIIdescribes several example electronic systems that implement someembodiments described herein.

I. Document Reader GUI

Some embodiments of the invention provide a document reader applicationwith an aggregated feed view that displays personalized documents (orarticles) for a user of the application. The aggregated feed viewincludes documents from a variety of different sources (e.g.,publishers, authors, etc.) and related to a variety of different topics.The aggregated feed view of some embodiments is divided into sectionsfor the personalized documents. In some embodiments, the differentsections are generated based on dynamically generated groups of thepersonalized documents.

In some embodiments, the personalized documents are identified at aserver based on user preferences (e.g., favorite topics or sources,reading habits, etc.). The personalized documents of some embodimentsare then analyzed and tagged based on the content of the documents. Theserver of some embodiments sends the tags for the personalized documentsto the client device, where they are dynamically grouped based on theassigned tags. The document reader application, at a client device(e.g., a mobile device, computer, etc.), then generates a layout for thepersonalized documents based on the document groups to be displayed forthe user. In some embodiments, the document reader also provides, in theaggregated feed, common content that is not based on the user'spreferences, but is provided to all users of the document readerapplication.

A. GUI for Document Groups

FIG. 1 illustrates an example of a GUI 100 for a document readerapplication that displays sections of common content. In someembodiments, the GUI 100 for the document reader application is designedto provide the aggregated content from various publishers with theappearance of a personalized magazine. GUI 100 includes masthead 110,document display area 120, and menu bar 130. Menu bar 130 of someembodiments includes icons for performing various operations in thedocument reader application, such as viewing different collections ofdocuments (e.g., aggregated feeds, source feeds, etc.), adding newsources or topics to the document reader application (e.g., adding to auser's favorites, subscribing to new sources, etc.), and otherwisenavigating through the document reader application. In this example, the“For You” icon of the document has been selected (shown in bold) todisplay an aggregated feed of personalized documents that have beenidentified for a user of the document reader application.

Masthead 110 shows various summary information for the user of thedocument feed. In this example, masthead 110 displays the date, weather,and a location. Alternatively, or conjunctively, masthead 110 displaysother summary information, such as sports scores, traffic alerts, feedrefresh notifications, etc. In addition, masthead 110 of someembodiments displays a signature logo 115 associated with the documentreader application to provide a distinctive appearance with signaturebranding for the view of the documents in the user's aggregated feed. Insome embodiments, the aggregated feed is divided into multiple differentsections (e.g., top stories, personalized group sections, etc.) andmasthead 110 provides section navigation controls (not shown) fornavigating to different sections of the aggregated feed.

In some embodiments, the masthead is displayed with different sizesbased on a user's interaction with the aggregated feed. The differentsizes allow the GUI 100 to provide relevant information to the userwhile also maximizing the space available for displaying the documentsof the aggregated feed. For example, in some embodiments, the fullmasthead 110 is shown at the top of GUI 100 when the application isopened to provide a full magazine experience. Masthead 110 is thenhidden as a user scrolls down through an aggregated feed to maximize thespace available for displaying the aggregated feed, and shown in aminimized state (e.g., a shorter view with smaller fonts and/or lessinformation) when the user scrolls back up through the aggregated feed.In some embodiments, when the user scrolls to the top of the aggregatedfeed, the minimized masthead returns to the full masthead 110.

The document display area 120 is a region of the GUI 100 that displaysdocument panes (or summaries) for the various documents of theaggregated feed. Each displayed document pane provides summaryinformation (e.g., an image, headline, excerpt, source, etc.) for anassociated document of the aggregated feed.

In some embodiments, document display area 120 displays the documentpanes of an aggregated feed in several different sections. The sectionsof some embodiments include common sections that display document panesthat are provided to all users of the document reader application, aswell as personalized sections that have been personalized for the user.In the example of this figure, document display area 120 includes a topdocument section 124, entitled “Top Stories”, and an algorithmicdocument section 128, entitled “Trending Stories”.

The top document section 124 of some embodiments displays document panesfor a curated (e.g., selected/approved by an editor) set of topdocuments that have been selected for all users of the document readerapplication. In this example, the top document section 124 displaysdocument panes for the top news stories of the day.

In some embodiments, the top document section 124 is the first sectionthat is displayed when the document reader application is opened. Insome such embodiments, the document reader application displays the topdocument section 124 in a signature layout that is unique to the topdocument section 124, providing a predictable and identifiable layoutassociated with the document reader application.

Top document section 124 also shows a user interface (UI) control 160 todisplay more top documents. In some embodiments, the UI control 160 isused to replace the documents displayed within the top document section124 with more top documents. In other embodiments, selection of the UIcontrol 160 provides a new feed view that displays additional topdocuments.

The algorithmic document section 128 of some embodiments is anothercommon section that includes documents that have been selected for allusers of the document reader application, but rather than a curated setof documents, the algorithmic document section 128 includes documentsthat have been programmatically selected based on interactions of otherusers of the document reader application. For example, the algorithmicdocument section 128 displays document panes for trending stories thatare growing in popularity with other users (e.g., based on the number oftimes a document is read, shared, liked, etc.).

In this example, the different sections 124 and 128 are separated by agray line. In different embodiments, the separation between differentsections is displayed differently. For example, in some embodiments,each section is colored differently. In other embodiments, the sectionsare displayed in a same color (e.g., white), but each section isdisplayed with a gradient, so that the bottom of each section isdifferent (e.g., darker) than the top of the next section.

FIG. 2 illustrates an example of a GUI 200 for a document readerapplication that displays sections for groups of personalized and commoncontent. In some embodiments, the personalized sections of GUI 200 are apart of the layout for the aggregated feed described above withreference to FIG. 1. In some embodiments, the layout for the aggregatedfeed starts with common content in order to allow for dynamic groupingfor the personalized content, as described in further detail below. Inthis example, GUI 200 shows a personalized group section 210, anadvertisement section 220, and a curated section 230.

The personalized group section 210 and the curated section 230 showdocument panes (depicted as white boxes) for associated documents. Inthis example, and in other examples of this application, the documentpanes are shown as simple white boxes. More detailed descriptions andexamples of the document panes are described below with reference toFIG. 4.

In this example, personalized group section 210 displays document panesfor several articles that are related to a particular topic (“DenverEagles”). The personalized group section 210 of some embodiments is oneof several personalized sections, in which each personalized sectiondisplays documents (or document panes) for a particular group of thepersonalized documents. For example, in some embodiments, thepersonalized documents are dynamically grouped based on tags associatedwith the personalized documents. The process for dynamically generatinggroups of documents for the personalized sections is described infurther detail below in Section II.

The documents of the group are positioned within the personalized groupsection 210 to form a layout for the personalized documents of thegroup. In some embodiments, the layout of the personalized section 210is divided into subsections 212, 214, and 216. The first subsection 212of some embodiments is a head subsection, while subsections 214 and 216are body subsections. In some embodiments, a head subsection is thefirst subsection of each section and includes a primary document panefor a primary document that is emphasized for the reader. Each sectionthen includes zero or more body subsections.

In some embodiments, the documents of the group are positioned withineach subsection based on a template that is selected from a group oftemplates. In some embodiments, the template for each subsection isselected based on the type of subsection (e.g., head or body), the setof documents to be displayed, the templates for the surroundingsubsections, etc. The process for generating layouts for a documentsection is described in further detail below in Section B.

In some embodiments, personalized group sections include variouscontrols for interacting with the group (or topic) of the personalizedsection, such as displaying additional documents related to the topic,disliking or blocking the topic (e.g., removing the particular topicfrom a whitelist of available topics), adding the topic to a list offavorite topics associated with the user. Personalized group section 210shows a user interface (UI) control 260 to display more articles relatedto the topic (“Denver Eagles”) of personalized section 210. In someembodiments, the UI control 260 is used to replace the articlesdisplayed within the personalized group section 210 with the additionalarticles related to the topic. In other embodiments, selection of the UIcontrol 260 provides a new feed view that displays the additionalarticles related to the topic. In some embodiments, some of the controlsfor interacting with the group (e.g., adding a topic to the list offavorite topics) are shown on the new feed view.

In addition to the personalized group section 210, GUI 200 shows anadvertisement section 220 that displays an advertisement for a musicservice. In some embodiments, advertisements are periodicallyinterspersed between the sections of the aggregated feed. In someembodiments, the placement (e.g., size, location) of an advertisementsection in the aggregated feed is based on an amount paid by theadvertiser. Alternatively, or conjunctively, the placement of theadvertisement sections may also be based on the surrounding personalizedsections. For example, in some embodiments, an advertisement section isrelated to a particular topic and is more likely to be placed near apersonalized section related to the particular topic.

In order to prevent a user from confusing advertisement sections withthe personalized content sections, the advertisement sections of someembodiments are visually distinguished from the personalized contentsections. In this example, advertisement section 220 is set apart withseparators (shown as gray lines) between the advertising section 220 andthe other sections 210 and 230. Alternatively, or conjunctively, theadvertising sections of some embodiments are visually distinguished by acolor scheme or style for the advertising sections.

GUI 200 also shows a curated section 230 for curated content (e.g.,content selected/approved by an editor). In some embodiments, curatedsections are dispersed through an aggregated feed to diversify thecontent in a user's feed. In some embodiments, the curated sectionsprovide documents for curated topics that are not likely to be permanentfavorite topics for a user. For example, curated topics may only berelevant for temporary periods of time (e.g., the Olympics, Iowacaucuses, etc.). The curated section 230 of some embodiments alsoincludes curated documents that are selected for their relevance to thecurated topic, while in other embodiments the documents are identifiedin other ways (e.g., programmatically, etc.).

Like the advertisement section 220, in order to prevent a user fromconfusing curated content sections with the personalized contentsections, the curated sections are visually distinguished from thepersonalized content sections. In some embodiments, the advertisementsections and the curated sections are visually distinguished from thepersonalized content sections in different ways. For example, thedifferent types of sections may use different color schemes, or one typeof section may use a particular color scheme while the other type usesseparators.

In some embodiments, the GUI 200 for an aggregated feed layout includesother types of sections. For example, in some embodiments, when thegroups for the different personalized sections are dynamicallygenerated, some of the articles cannot be grouped, such as when they donot have enough related articles to form another group. In some suchembodiments, the GUI 200 includes an ungrouped section (or an orphansection) for any articles that are not a part of the groups of thegrouped sections.

In some embodiments, the document reader application displays a specialfeatured article section for featured articles as a part of theaggregated feed. The featured articles of some embodiments includepremium content from different publishers, content requiring asubscription, content which the publishers have paid to promote, etc.FIG. 3 illustrates an example of a featured article group in four stages301-304. The first stage 301 shows the bottom end of a GUI 300 for adocument reader application. The first stage 301 also shows articles 310and 312 of a featured article group, which are currently off-screen, butready to be displayed in a featured articles section of GUI 300. In thefirst stage 301, a user slides up in the GUI 300 to show more of theaggregated feed.

The second stage 302 shows that the first article 310 of the featuredarticle group expands into the featured article space of GUI 300. Insome embodiments, the transition of the first article 310 is animatedonto the GUI 300 as the user slides through the aggregated feed. In someembodiments, the document panes include multi-layer imagery, which areused to create a parallax effect when animating the document pane intothe GUI 300. The parallax effect provides the impression of depth andmovement in a two-dimensional image.

In the third stage 303, the first article 310 is displayed in thefeatured articles section. In some embodiments, unlike the otherdocument sections, the featured article section only displays (oremphasizes) a document pane for a single document at a time. The formatof the document panes of the featured articles section is also differentfrom the document panes displayed in the other document sections in someembodiments. For example, in some embodiments, the document panes forthe featured articles are displayed with a single, large image withoverlaid descriptive text, similar to a cover of a magazine.

In this example, a portion of the next article 312 is also shown,indicating that another article is available and showing a portion ofthe associated document pane. In some embodiments, as the document panesfor the featured articles are full images, the document panes haveaspect ratios associated with the image. The document pane for the firstarticle 310 has a horizontal format (e.g., a 4:3 aspect ratio). Thethird stage 303 shows that the user swipes to the left to show the nextfeatured article 312. In some embodiments, the transition betweenarticles uses an animation similar to the animation used to bring thefirst article into the display area (e.g., creating a parallax effect).

The fourth stage 304 shows that the next featured article 312 isdisplayed in the GUI 300. The document pane for the next article 312 hasa vertical format (e.g., a 3:4 aspect ratio). In some embodiments, thedocument reader application displays the document panes for the featureddocuments with varying aspect ratios.

In some embodiments, a user navigates between the different documentsections (e.g., common and personalized group sections) and through thecontent of each document section in a first direction (e.g., up-down),but navigates through the document panes of a featured article sectionin a different, second direction (e.g., left-right). In someembodiments, the second direction is orthogonal to the first.

In this example, GUIs 100, 200, and 300 show particular examples ofdifferent sections in an aggregated feed layout. It should be understoodthat this is not meant to limit the invention in any way. The differenttypes of sections (e.g., personalized sections, advertisement sections,curated sections, etc.) can be displayed in various orders andcombinations.

B. Section Layout Elements

In some embodiments, layouts for the sections of the aggregated feed aregenerated to provide a great user experience. Document panes provide arepresentation (or summary) for the documents of each section. Eachsection of some embodiments is made up of multiple subsections. In someembodiments, templates are selected for each of the subsections toposition the document panes within each subsection.

FIG. 4 illustrates four examples of different formats for documentpanes. Document panes of some embodiments provide summaries ofinformation related to a document. For example, for an article, thedocument pane may include one or more of an image associated with thearticle, a publisher (or source) of the article, a title of the article,and an excerpt (or summary) from the article. The different formats ofthe document panes provide different elements from the document,different positioning layouts for the elements, and different sizes.

The first example document pane 401 shows a document pane with an image410, source information 420, title 430, and an excerpt (or briefsummary) 440. The image 410 of some embodiments is one of several imagesassociated with the document. For example, image 410 of some embodimentsis a still image associated with a video document, a cover image for anarticle, etc. In some embodiments, the image 410 is modified (e.g.,cropped, stretched, etc.) to fit a particular aspect ratio (e.g., 3:4,4:3, 1:1, etc.) in order to maintain a pleasing appearance for thedocument panes in the aggregated feed layout.

The aggregated document feed of some embodiments includes documents froma variety of different sources. The source information 420 allows a userto quickly determine the source of a particular article. The sourceinformation 420 of some embodiments includes a logo and a source name(“Newz”). The logo and source name are provided by the source (e.g., thepublisher or author) of the article. The excerpt 440 is a short excerptor summary from the associated document.

In some embodiments, the document panes also include other information.For example, some embodiments include a related time value (e.g., thetime since the article was retrieved or published), a statusnotification (e.g., breaking, trending, etc.), etc. Although this figureshows four examples of document panes, one skilled in the art willrecognize that many other types of document panes with differentlayouts, sizes, information, and formats could be imagined.

The second example document pane 402 shows the same image 410, source420, title 430, and excerpt 440. In this example, however, the layoutpositions the image 410 to the left of the document pane 402 andprovides the source 420, title 430, and excerpt 440 to the right side.In some embodiments, the same image 410 can be reformatted (e.g.,cropped, resized, etc.) to fit in the different document panes withdifferent aspect ratios.

The third example document pane 403 shows the same image 410 and source420, but in this example, the title 430 is shortened and no excerpt isdisplayed. In some embodiments, document panes include different amountsof information from the document. For example, in the fourth exampledocument pane 404, only the source 420 and title 430 are displayed. Thedocument panes of some embodiments are formatted and laid out to fitwithin templates for the different sections of the aggregated feed. Thevarious formats and layouts can be mixed and matched to create apleasing visual aesthetic.

The document reader application of some embodiments uses templates toposition the document panes within a group section. In some embodiments,each group section is made up of one or more subsections, and eachsubsection has its own template to define the layout for the documentpanes of the subsection. Each template of some embodiments spans thewidth of the display area and defines the placement of each documentpane within the subsection. Different templates may include a differentnumber of document panes with different sizes, formats, and layouts.

In some embodiments, a group section includes a head subsection and zeroor more body subsections. The head subsection of some embodiments is thefirst subsection and templates for the head subsection provide differentlayouts than those of the body subsection templates. FIG. 5 illustratesthree examples of different head subsection templates. Different headtemplates may include a different number of document panes withdifferent sizes, formats, and layouts.

The first example template 501 shows a header template with a singleprimary document pane 510. In some embodiments, each head templateincludes a primary document pane for a primary document. The primarydocument of some embodiments is a highest-ranked document (e.g., basedon user preferences, popularity with other users, relevance to the topicof the group, etc.)

In some embodiments, the primary document panes occupy at least half ofthe template width and the full template height. The primary documentpane of some embodiments is the largest document pane to emphasize theprimary document. In some embodiments, the primary document pane alsouses different appearances (e.g., a different color scheme or othervisual indications) to further emphasize the primary document within aparticular section. In some embodiments, the primary document pane isthe only document pane to provide an excerpt (or summary) from theassociated article.

The second example template 502 shows a header template with a primarydocument pane 520 and a set of secondary document panes 522-526. In thisexample, the primary document pane 520 is the largest document pane inthe template, but also provides document panes for other documents(i.e., secondary documents). The secondary document panes can bepositioned in a variety of different ways with a variety of differentsizes and formats to fit with the primary document pane 520.

In some embodiments, two primary documents may be selected for aparticular group of documents. The third example template 503 shows aheader template with two primary document panes 530 and 532.

FIG. 6 illustrates three examples of different body layout templates.Different head templates may include a different number of documentpanes with different sizes, formats, and layouts. The first exampletemplate 601 shows a body template with three similarly sized documentpanes 610-614. In some embodiments, the document reader applicationprefers layouts with similarly sized document panes when the documentshave images with a same aspect ratio and headlines of similar length.

The second example template 602 shows a body template with documentpanes 620-626, which are of different shapes and sizes. The secondexample template 602 fits four document panes, rather than the threedocument panes of the first example 601. In some embodiments, aparticular template is designed to facilitate the alignment of theimages of the different document panes. For example, in this example,template 602 may be designed for use when document pane 622 has a 4:3aspect ratio image and text, while document pane 624 has a full-height1:1 aspect ratio image, allowing the heights of the images of 622 and624 to align with each other. In addition, the distribution of thedocument panes in the template divides the template in halves, ratherthan the thirds of the first template 601. The alignment of thedifferent divisions of the templates affects the continuity of thesection.

The third example template 603 shows a body template with six spaces630-640. This example allows for more documents to fit in a singlesubsection, providing flexibility when arranging the documents for aparticular section. In this example, the half-height document panes632-638 may only provide a title for the corresponding documents, whiledocument panes 630 and 640 include images as well.

C. Generating Section Layouts

FIG. 7 conceptually illustrates a process 700 for generating a layoutwith group sections for an aggregated feed. In some embodiments, theprocess 700 is performed by the device on which the feed is displayed.The process 700 begins by receiving (at 705) a set of document groupsfor dynamically grouped documents of an aggregated feed. Dynamicallygrouping documents into document groups is described in further detailbelow in Section II.

In some embodiments, the set of document groups includes all of thedocuments of the aggregated feed. However, in some embodiments, the setof document groups includes only a portion of the documents of theaggregated feed. For example, when a document reader applicationprovides a section of ungrouped personalized documents, as describedbelow with reference to FIGS. 11-13, the process 700 of some embodimentsgenerates a layout with group sections for the remaining personalizeddocuments.

The process 700 then selects (at 710) a document group from the receivedset of document groups for a next section of the feed portion. Thedocument group for the next group is selected in various ways indifferent embodiments. In some embodiments, the groups are selectedrandomly. In other embodiments, the document groups are selected basedon the topics to which each group relates.

In some embodiments, process 700 selects (at 710) the document groupaccording to an order specified for the document groups. The ordering ofsome embodiments is based on properties of the various document groups.For example, in some embodiments, the ordering of the document groups isbased on the number of documents in each document group. Alternatively,or conjunctively, the document groups of some embodiments are associatedwith a topic (or tag) and the ordering of the document groups is basedon the associated topic (e.g., alphabetically, based on a relevance ofeach topic to the user, etc.).

In some embodiments, the document groups are ordered based on propertiesof the documents within the document groups. For example, the groupordering of some embodiments is based on the popularity or relevance ofthe documents for the particular user and/or for other users of thedocument reader application. Alternatively, or conjunctively, theordering of some embodiments are based on the dates that documents in adocument group are published (e.g., groups with recent documents areprioritized over groups with older documents). The group ordering ofsome embodiments is based on visual elements of the documents in thegroup (e.g., groups with many images are prioritized over groups withoutimages, etc.).

In some embodiments, the ordering of the document groups (or topics) isaffected by the other document groups in the set. For example, in someembodiments, groups at different levels of a hierarchy (e.g., sports isa higher level (i.e., more general) topic than basketball) are orderedso that lower level topics appear before the more general, higher leveltopics. In some embodiments, the document groups are ordered tointersperse unrelated groups between related groups to provide varietyas a user navigates an aggregated feed.

Once a group has been selected (at 710), the process 700 then generates(at 715) a layout for the selected document group based on a set ofsubsection templates. As described above with reference to FIGS. 5 and6, the subsection templates of some embodiments include head templatesand body templates that are used to position document panes for thedocuments of the document group within a section layout. In someembodiments, the section layout with the subsection templates isgenerated based on a variety of variables (e.g., number of documents inthe document group, whether the documents have images, aspect ratios ofany images, position of the image within a document pane, title length,etc.).

In some embodiments, the process 700 generates (at 715) the layout byselecting a single head template from a set of static head templates andzero or more body templates from a set of static body templates. In someembodiments, the layout uses different sets of static head/bodytemplates based on a size or resolution of a display area for the device(e.g., mobile phone, tablet, computer, etc.). In some embodiments, thesize of a display area is defined in terms of a number of columns. Forexample, a first device (e.g., a mobile phone) may use an 8 columnlayout, while a second device (e.g., a tablet) uses a 12 or 16 columnlayout. In some such embodiments, the document reader applicationselects sets of head and body static templates specific to each columnlayout.

In some embodiments, the layout is generated in a manner that positionsdocuments within the section to provide a visual effect with adescending level of visual density. Positioning documents with highervisual density at the top of each section provides a pleasing visualeffect that draws a user's attention through the different sections ofthe aggregated feed. The ranking of documents for visual density isdescribed below with reference to FIG. 8.

Once the layout for the selected group has been generated (at 715), theprocess 700 computes (at 720) an intra-group score for the generatedlayout. In some embodiments, the intra-group score is used to determinewhether the generated layout is acceptable. The different shapes andsizes of the head templates and body templates allow for variouscombinations of the different documents within each template.

In some embodiments, the intra-group score is based on the subsectiontemplates of the generated layout. In some embodiments, the process 700computes (at 720) the intra-group score by evaluating transitionsbetween a set of templates based on how each template lines up with theone before it. For example, it is undesirable in some embodiments tohave an edge of a document pane of a first template that is near, butdoes not align with, a document pane of a second template. In someembodiments, a transition is favored when the edges of document panes inthe first template either align with edges of the second template orhave a minimum distance between the edges of the first template and theedges of the second template.

In some embodiments, the process 700 not only evaluates individualtransitions between a pair of templates, but also evaluates eachtransition based on the previous one or more transitions. For example,in some embodiments, the transition between a particular body subsectionand itself (e.g., repeated use of the particular body subsection) is adesirable combination. However, as the combination repeats, thetransition becomes less desirable as excessive repetitions of the samesubsection template becomes visually uninteresting.

In addition to the intra-group score, the process 700 also computes (at725) an inter-group score for the generated layout based on layouts ofthe previous groups. For the first group of the layout, as there are noprevious groups, no inter-group score is computed. In some embodiments,the inter-group score evaluates the generated layout in view of thelayouts of the previous groups. In some embodiments, the process 700computes (at 725) the inter-group score to evaluate the transitionsbetween the generated layout and the previous one or more layouts in amanner similar to the evaluation of the transitions between thesubsection templates described above. For example, in some embodiments,the inter-group score is computed to prevent the repetition of a samelayout for multiple sections in a row or to prefer (or promote)particular transitions between the last body subsection template of theprevious section and the head template of the generated section.

The process 700 then determines (at 730) whether to use the generatedlayout for the group. In some embodiments, the process 700 determines(at 730) to use a generated layout when the intra-group and inter-groupscores for the layout exceed a particular threshold value or when allpossible section layouts have been evaluated. In some embodiments, theprocess 700 determines (at 730) to use a generated layout after aparticular period of time has passed or after a particular number ofattempts. In such embodiments, the process 700 will use the bestgenerated layout (based on the inter-group and intra-group scores) thatis available when the period of time has passed or the number ofattempts have been completed. In some embodiments, rather thangenerating multiple layouts for each group, the generation (at 715) ofthe layout uses a single-pass process to produce a single layout to beused for the document group and the process 700 always determines (at730) to use the single layout.

When the process 700 determines (at 730) not to use the generatedlayout, the process 700 returns to step 715 to generate another layoutfor the document group. In some embodiments, the layouts generated (at715) differ in at least one of the set of subsection templates used, theformat of the individual document panes, or the order of the documentpanes in the section.

When the process 700 determines to use the generated layout for thegroup, the process 700 determines (at 735) whether there are more groupsfor which layouts need to be generated. When the process 700 determines(at 735) that there are more groups, the process 700 returns to step 710to select another group for the next section of the aggregated feedportion. Otherwise, the process 700 ends.

As described above, the document reader application of some embodimentsarranges document panes for documents within a section of the aggregatedfeed based on visual aesthetics of the different document panes. FIG. 8conceptually illustrates a process for ranking document content based onvisual aesthetics. The process 800 begins by determining (at 805)whether the document has an image. When the process determines (at 805)that the document does not have an image, the process 800 determines (at810) whether the headline is a long headline. In some embodiments, along headline is any headline that exceeds a threshold number ofcharacters. When the process 800 determines (810) that the headline isnot a long headline (i.e., the document has no image and a shortheadline), the process 800 assigns (at 815) a lowest rank (rank 5 inthis example). When the process 800 determines (at 810) that theheadline is a long headline, the process 800 assigns (at 820) a rank of4. In some embodiments, long headlines are preferred to short headlinesbecause they provide a level of visual density for the document panesand avoid creating too much whitespace within a layout.

When the process 800 determines (at 805) that the document does have anassociated image, the process 800 determines (at 825) whether thedocument has a headline. When the process 800 determines (at 825) thatthe document does not have a headline, the process 800 assigns (at 830)a rank of 3. In this case, a document with an image, even with noheadline, is preferred over any document without an image.

When the process 800 determines (at 825) that the document does have aheadline, the process 800 determines (at 835) whether the headline is along headline. The process 800 then assigns a rank of 2 when theheadline is short, and a rank of 1 when the headline is long. Theprocess 800 then ends.

The process described in this example assigns different numericalrankings, but the numerical rankings are merely for illustration. Therankings are used to describe an example of the different documentpreferences when arranging the document panes for a section, but are notmeant to limit the invention to a particular ranking system or in anyother way. In some embodiments, the rankings are used to create a visualhierarchy of documents, emphasizing documents with higher visualdensity. For example, in some embodiments, the highest ranking articlesare provided in the header, while the lower ranking articles areprovided in descending rows beneath it so that the visual density of theaggregated feed slowly transitions to lower levels of density as theuser proceeds through a section.

In some embodiments, the document reader application uses a contentscore (e.g., based on relevance to a user, interaction with the documentby other users, etc.) to select the primary document, but uses thevisual rankings to order the other documents of the section. In otherembodiments, the content score is used in combination with the visualdensity rankings. For example, for various articles with the sameranking, the content score is used to place more relevant documentshigher within the section.

D. Marking Tools in a Multi-Pane Layout

The document reader application of some embodiments provides differentinterfaces for different types and sizes of devices. For example, insome embodiments, the document reader provides a larger interface fortablet type devices and a smaller interface for other mobile devices(e.g., mobile phones, etc.). In some embodiments, particularly withsmaller display areas, it is difficult to provide various tools forinteracting with the documents without cluttering the screen. In someembodiments, the tools for interacting with the documents are hidden,and then provided upon further input (e.g., a swipe input) from theuser. In some cases, the layouts for the smaller devices include atwo-pane layout, in which a particular horizontal section displays twoadjacent document panes.

FIGS. 9 and 10 illustrate two examples of providing tools forinteracting with the documents in a two-pane layout. FIG. 9 illustratesan example of providing UI tools for marking documents in a two-panelayout in two stages 901 and 902.

The first stage 901 shows GUI 900 with various document panes. Inparticular, GUI 900 includes document panes 905 and 910, which aredisplayed in a two-pane layout. In the first stage 901, the user slidesto the right across document pane 905. The document pane 905 slides tothe right, pushing document pane 910 off-screen.

In the second stage 902, document pane 905 has been pushed to the rightside of the screen, partially off-screen, to make room for the UIcontrols 940. In addition, UI controls 940 slide in from the left tofill in the space made by pushing document pane 905 to the right. The UIcontrols 940 are for performing various operations on the documentassociated with document 905. In some embodiments, the UI controls 940include UI controls for sharing the document, liking/disliking thedocument, saving the document to be read later, etc.

FIG. 10 illustrates another example of providing UI tools for markingdocuments in a two-pane layout. The example of this figure is similar tothe example of FIG. 9, but rather than sliding to the right, the firststage 1001 shows that the user slides to the left across document pane905. The document pane 905 slides to the left, pushing document pane 910off-screen. In some embodiments, pushing from the middle of the layoutto an edge slides the other document pane 910 in the opposite direction(e.g., to the right) and off-screen. In other embodiments, slidingdocument pane 905 to the left causes document pane 910 to slide to theleft, beneath document pane 905, and off-screen.

UI controls 940 slide in from the right to fill in the space made bypushing document pane 905 to the left. In the second stage 1002,document pane 905 has been pushed to the left side of the screen,partially off-screen, to make room for the UI controls 940. The UIcontrols 940 represent various different controls for interacting withthe document 905. In some embodiments, the UI controls 940 include UIcontrols for sharing the document, liking/disliking the document, savingthe document to be read later, etc.

E. Dynamic Group Layouts

In some embodiments, the group layouts for a user's personalized contentare generated dynamically by the document reader application at a clientdevice. In order to provide a responsive interface, the document readerapplication of some embodiments will be ready to provide thepersonalized content, even when a group layout has not been generated.

FIG. 11 illustrates an example of providing an initial ungrouped sectionof personalized content in two stages 1101 and 1102. The first stage1101 shows GUI 1100 with common content sections 1110 and 1120. Thecommon content sections provide a curated top stories section 1110 andan algorithmic trending stories section 1120, as described above withreference to FIG. 1. In the first stage 1101, the user provides input(slides up) to show more of the aggregated feed.

The second stage 1102 shows that the common content sections 1110 and1120 have moved up to show a latest stories section 1130. In thisexample, the user input is received before an acceptable layout isgenerated (or when no acceptable layout is identified), so the secondstage 1102 shows that the GUI 1100 displays an ungrouped section 1130(Latest Stories) of personalized stories. In some embodiments, theungrouped section 1130 displays documents that are not grouped based onany particular topic, but are selected from personalized documentsdesignated to be displayed in the aggregated feed. In some embodiments,the documents of the ungrouped section 1130 are a set of the highestranked stories (e.g., based on user preferences) for the user of thedocument reader application.

In some embodiments, the latest stories section 1130 is a preliminaryungrouped section that is displayed while a better grouped layout isbeing identified for the remaining articles that are left to bedisplayed. In some such embodiments, the articles displayed in theungrouped section 1130 are removed from the grouping process, and thegrouping process restarts and attempts to create a grouped layout forthe remaining articles. The dynamic grouping process is described infurther detail below in Section II.

FIG. 12 illustrates an example of providing grouped personalizedcontent. The example of this figure is similar to the example of FIG.11, however, in this example, the user input is not received until afterthe grouping process is able to identify an acceptable grouped layout.In some embodiments, the GUI 1100 initially provides a region of commoncontent (e.g., common document sections, algorithmic sections, curatedsections, etc.) before any personalized content in order to secure sometime to generate a grouped layout.

In the second stage 1202, in response to the user input received in thefirst stage 1201, the common content sections 1110 and 1120 have movedup to show a group section 1230. As an acceptable layout is generatedbefore the user input is received, the second stage 1202 shows that theGUI 1100 displays the personalized group section 1230 (“Sports”) of theuser's personalized content instead of the latest stories sectiondescribed in FIG. 11.

In some embodiments, the document reader application generates multiplegrouping layouts for the personalized documents and selects the bestavailable layout to be displayed when the user requests the personalizeddocuments (e.g., by sliding through the aggregated feed). FIG. 13illustrates an example of generating different group layouts to minimizeorphans in three stages 1301-1303.

The first stage 1301 shows an initial ungrouped layout 1310 (“LatestStories”) for the personalized documents. In some embodiments, theinitial ungrouped layout 1310 is a default layout that places thepersonalized content in a single ungrouped section, irrespective to anytags or topics to which the documents are related.

In the second stage 1302, the document reader application has generatedan acceptable group layout. The second stage 1302 shows a group layoutwith a grouped section 1320 and an ungrouped section 1330. The groupedsection 1320 shows a set of document panes for documents related to aparticular topic (“Sports”). The ungrouped section 1330 shows a set ofdocument panes for documents that could not be grouped. For example, thedocuments may not have had enough related articles to form another groupof a minimum size.

However, in some embodiments, the document reader application continuesto look for a better grouping solution. For example, in someembodiments, the application continues to search for a better solutionfor a designated period of time (e.g., up to 50 ms), while in otherembodiments, the application continues to search for better solutionsuntil the user input is received.

The third stage 1303 shows an improved grouped layout with groupedsections 1340 and 1350, along with an ungrouped section 1360. Thegrouped layout of the third stage 1303, in some embodiments, ispreferred to the layout of the second stage 1302 because it only hasthree orphan documents in the ungrouped section 1360, while the solutionof the second stage 1302 has five orphan documents in ungrouped section1330. In some embodiments, the ranking of the grouping layouts is basedon other factors, such as a number of groups produced, an evenness inthe distribution of the documents across the different groups, adiversity of the groups produced, etc.

FIG. 14 conceptually illustrates a state diagram for a document readerapplication that provides grouped personalized content. Specifically,the state diagram 1400 refers to the display of common and personalizedcontent in a document reader application. As shown, the applicationbegins in state 1405, in which the application displays common contentin the aggregated feed view. In some embodiments, the application entersstate 1405 when the document reader is first opened, while in otherembodiments, the application enters state 1405 from another view (e.g.,a login screen, a favorites view, etc.) within the application.

From the state 1405, the application attempts to generate an initialgroup layout for the personalized articles of the aggregated feed. Whenthe application succeeds in generating an initial group layout, theapplication transitions to state 1410, in which it sets the generatedgroup layout as an active layout. Each group layout of some embodimentsincludes multiple group sections for different groups of documents, aswell as an ungrouped section for orphan documents that are not assignedto any group. At state 1410, the program does not yet display the activelayout, but rather continues to display the common content that wasdisplayed in state 1405.

While in state 1405, the application continues to generate new grouplayouts for the personalized articles. When the application generates animproved layout (e.g., a layout with fewer orphans), the applicationreturns to 1410 and sets the newly generated group layout as the activelayout. From state 1405, when the application receives user input (e.g.,a swiping touch input) to display the personalized articles, theapplication moves to state 1425 and displays the active layout of thepersonalized content.

When the application is displaying the common content in 1405, if theapplication receives the user input before an initial group layout canbe generated, the application moves to state 1415 and displays a set ofpersonalized content for an ungrouped layout. In some embodiments, theapplication continues to try to generate an initial grouped layout,using the remaining personalized content that was not included in theungrouped layout.

When the application succeeds in generating an initial layout with theremaining documents, it transitions to the state 1410 and sets thegenerated group layout as the active layout for the remainingpersonalized documents. The application then attempts to improve thelayout or transitions to state 1425 to display the active layout asdescribed above. In such a case, the layout for the personalizeddocuments may include an ungrouped section before the grouped sectionsand another ungrouped section for orphans after the grouped sections.

When the application fails to generate a layout, either because theoperation times out or because no acceptable group layout can begenerated, the application transitions to state 1420, where it sets anungrouped layout as the active layout. When the user input is received,the application transitions to state 1425 to display the ungroupedlayout as the active layout.

II. Dynamic Grouping of Documents

As mentioned, some embodiments display documents that are dynamicallyarranged in groups below the static content (e.g., the trending content,top stories, etc.). In some embodiments, the device receives a number ofdocuments (e.g., new documents since the last time the user has viewedthe document feed) and dynamically arranges these documents into thegroups for display in the feed. The documents as received are assignedone or more tags (e.g., by the set of servers that provide thedocuments) that each indicate relevancy to a topic, and these tags areused for the groups in some embodiments. Of all the possible tags thatare assigned to the received documents, the device identifies some orall of the tags for use in grouping the documents. The device thendynamically assigns the documents to groups for at least a subset ofthese identified grouping tags. In some embodiments, this dynamicgrouping involves iteratively selecting tags and assigning some or allof the documents assigned that tag to a group for the tag, then removingthe assigned documents for the next iteration. With the documentsassigned to groups, the device adds the groups of documents to the feed(e.g., below the trending topics). Documents that are not assigned toany group are displayed below the groups in the feed (e.g., in the “MoreFor You” section of the feed).

FIG. 15 conceptually illustrates a set of documents 1500 along withmultiple possible groupings 1505-1515 for those documents. In someembodiments, the goal of the grouping process is to find an optimalgrouping that minimizes the number of documents that are left ungrouped,for a minimum (and maximum) number of documents that can be assigned toa group. The iterative process is dynamic, such that a choice of whichtag to select or which documents to select for a group based on theselected tag will affect the next tag selected, and may affect the setof documents left unassigned at the end.

In FIG. 15, the set of documents 1500 includes eight documents (labeledDoc 1-Doc 8) that are each assigned one or more of the tags A, B, and C.The figure illustrates three possible groupings 1505-1515 with a minimumof three documents in a group, though numerous other groupings arepossible. In the first grouping 1505, the tag C is selected on the firstiteration, and four of the documents (Docs 5-8) are assigned to thegroup for tag C according to a set of heuristics (which may use randomselection to break ties between otherwise even options). Next, tag A isselected for the second iteration, and three of the remaining documents(Docs 1, 3, and 4) are assigned to the group for tag A. However, thisleaves one document (Doc 2) without a group.

In the second grouping 1510, the tag B is selected on the firstiteration, and four of the documents (Docs 1, 2, 4, and 6) are assignedto the group for tag B according to the set of heuristics. Next, tag Cis selected for the second group, and the grouping process assigns thethree remaining documents (Docs 5, 7, and 8) that are assigned tag C tothis group. Again, this leaves one document (Doc 3, in this case)unassigned.

For the third grouping 1515, the tag A is selected on the firstiteration, and four of the documents (Docs 1, 3, 4, and 8) are assignedto the group for this first tag according to the set of heuristics.Next, tag B is selected for the second group, and the grouping processassigns all four of the remaining documents (Docs 2 and 5-7) to thissecond group. This last grouping option 1515 does not leave anyunassigned documents, and is thus an optimal solution of the three. Aquick examination finds that there are several other groupings (e.g.,Doc 5 could be switched from the tag B group to the tag A group, and Doc1 and/or Doc 4 could be switched from the tag A group to the tag Bgroup).

Because this is a simple example with only eight documents and threetags, finding an optimal solution with no unassigned documents is easy.However, for larger numbers of documents with more tags, minimizing theunassigned documents may be much more difficult (and computationallyintensive). In fact, the general problem is such that in someembodiments it is not computationally feasible to explore the entiresolution space (it is a NP-hard problem). Thus, some embodiments use analgorithm for grouping that uses heuristic move-ordering, heuristicposition-evaluation, and brute force endgames to evaluate solutions in astyle similar to single-player game strategy evaluations.

For the algorithm, notions of a position (a partial allocation ofdocuments to groups, within the allowable range of group sizes) and amove (assignment of some articles to groups, which is a change in statefrom one position to another position) are defined, as well as a fastand simple move-ordering heuristic (e.g., minimizing the number ofdocuments that will not be assignable to any group as a result of amove, referred to as orphans). In addition, the algorithm uses aprobabilistic move evaluation function (e.g., the number of orphansremaining after a greedy algorithm is run that approximates theremaining moves in some embodiments).

From a single player game strategy perspective, the algorithm starts atan initial position (no articles yet allocated to groups) and selects atop K moves m₁, m₂, . . . m_(K) based on the move heuristic, which leadto corresponding positions P₁, P₂, . . . P_(K). For each of thesepositions, the algorithm computes the evaluation function, and executesthe move with the best result from the evaluation function to advance tothe next position (i.e., a selection of a set of documents for a groupbased on a particular tag). This process is repeated until sufficientlyfew articles remain to be grouped that a full exhaustive search can berun (in some embodiments, the exhaustive search is run with dynamicprogramming to avoid duplication). Because the algorithm is based on aprobabilistic evaluation function and evaluating only a limited (K)number of moves (which may be selected randomly when numerous moves allhave the same heuristic value), some embodiments rerun the algorithmmultiple times (e.g., with increasing values of K) in order to identifyprogressively better solutions. The algorithm can exit either after afixed time, or when necessary because the user has scrolled down to viewthe groups.

FIGS. 16A-B conceptually illustrate a process 1600 of some embodimentsfor performing such dynamic grouping operations. This process 1600 isperformed on a device (e.g., a smart phone, tablet, or other userdevice) in some embodiments, when a user of the device attempts to viewnew documents in a feed. One of ordinary skill in the art will recognizethat this process 1600 is merely one possible process for dynamicallygrouping documents on a device, and variations on this process (orwholly different algorithms) may also be used to group documents for thedocument feed of some embodiments.

As shown, the process 1600 begins by receiving (at 1605) a set ofdocuments, each of which has been assigned one or more tags. In someembodiments, the device receives a set of documents that are new sincethe last time the feed was accessed. This may be a large number ofdocuments, and the device then selects a subset of these documentsaccording to a personalization algorithm. The personalization algorithmof some embodiments personalizes the feed according to past historyand/or specified preferences of the user of the device. Some embodimentsselect up to a fixed number of documents using the personalizationalgorithm (e.g., 25, 60, 100, etc.), or select a fixed percentage of thenumber of new documents (up to a maximum, in some cases). Thepersonalization processes of some embodiments are described in greaterdetail in U.S. Provisional Applications 62/276,919 and 62/310,751, whichare incorporated herein by reference, and in concurrently filed U.S.patent application Ser. No. 15/273,943, entitled “Document Selection andDisplay Based on Detected Viewer Preferences”, which claims priority tothese two provisional applications and is incorporated herein byreference.

Each of the selected documents (and the unselected documents, for thatmatter) are assigned zero or more tags, which in some embodimentsidentify that the document relates to a particular topic. In someembodiments, the tag assignment is performed by the servers thatamalgamate the documents and distribute the documents to the userdevices. In general, documents will have more than one tag, as the tagsmay range from specific (e.g., a specific person) to general (ahigh-level topic such as politics, sports, etc.). Furthermore, someembodiments may include tags for categories other than topics, such asthe source of a document or other category.

The process 1600 determines (at 1610) minimum and maximum numbers ofdocuments that are allowed in a group (min and mar), a maximum branchingfactor (k), and a set of whitelisted tags. These variables used by thedynamic grouping optimization algorithm may be defined in the code ofthe device, or may be changed from time to time on the back-end servers(and pushed to the device). The whitelisted tags are a set of tagsdefined for use in grouping. Like the algorithm variables, thiswhitelist may be predefined in the code of the device, or updated by theset of servers as different topics become more popular within thearticles provided by the set of servers, and provided to the device. Insome embodiments, the whitelist primarily contains more general tags forhigh-level topics in favor of very specific tags. It should beunderstood that this operation 1610 does not necessarily happen at thisparticular stage in the process, as these variables might already beknown, rather than determined at a specific operation.

The process then sets (at 1612) a branching factor (referred to as k)to 1. This branching factor, as described in more detail below,determines how many possible groupings will be evaluated using aheuristic algorithm (e.g., the greedy evaluation algorithm mentionedabove) at each iteration of the process. Some embodiments use a constantbranching factor for each run through the documents, as in the process1600, while other embodiments use processes that vary the branchingfactor within a run. That is, the process 1600 increments k after all ofthe documents have been assigned to groups (or left unassigned). Otherembodiments, however, may set a pattern of k-values for differentiterations within an assignment of documents to groups (e.g., startingat larger values of k, but decreasing for later iterations when fewerdocuments and tags remain).

The process 1600 then begins the actual iterative algorithm of someembodiments. The process identifies (at 1615) tags (from the remainingtags, if not on the first iteration) that have at least min documentsand that are on the whitelist. These are the set of tags to be used forgrouping, also referred to as groupable tags. The min value may be basedon a minimum number of documents that have been determined to berequired to form an aesthetic group in the documents feed (e.g., 3, 5,etc.). In some embodiments, the whitelist check is performed prior tothe iterative aspects, while the minimum number of documents may removetags from consideration after one or more iterations through thegrouping operations.

With the groupable tags identified, the process 1600 iteratively selectsa tag to use to form a group, identifies one or more potential groups ofdocuments for the tag (out of all of the documents that are assigned thetag), uses a set of heuristics to select one of the identified potentialgroups, and assigns the documents in the selected potential group to agroup for the selected tag. The device then removes the documents in theselected group for the next iteration. In some embodiments, the goal ofthe iterative process is to minimize the number of documents that areleft unassigned to any group. In assigning documents to groups, someembodiments ensure that each group has at least a minimum number ofdocuments (e.g., 3, 5, etc.) and no more than a maximum number ofdocuments (e.g., 10, 12, 20, etc.), in order to form aestheticgroupings.

To select a tag for a particular iteration, the process 1600 computes(at 1620), for each groupable tag, a ranking score based on propertiesof the documents that are assigned the tag. In some embodiments, thisscore factors in at least (i) a number of documents that are assignedthe tag (the size of the tag) and (ii) a number of documents for whichthat tag is the only tag assigned (referred to as “solos”). For example,some embodiments calculate the score as the number of solos multipliedby a first weighting factor plus the size multiplied by a firstweighting factor. In some embodiments, these weighting factors aredetermined by running machine learning algorithms on historical userdata.

Some embodiments may also use other factors relating to the tag (e.g.,personalized relevancy scores of the documents assigned the tag)multiplied by other weighting factors. For a simpler formula, someembodiments effectively use the number of solos as a primary score andthe size (e.g., smallest number of documents or largest number ofdocuments) as a tiebreaker. If multiple tags have the same score, someembodiments randomly order the tied tags. As a result of such randomselections, running the iterative process multiple times may yieldprogressively better results (or occasionally worse results, in whichcase the new results will not be used). The process then selects (at1622) the tag with the highest score for the next group.

FIG. 17 conceptually illustrates this calculation for the set ofdocuments 1500 from FIG. 15. As shown, there are three groupable tags(A, B, and C) in this example. With most of the documents (all but Docs2 and 3) having multiple tags assigned, the computation identifies thattag A is assigned to 5 documents including 1 solo, tag B is assigned to6 documents including 1 solo, and tag C is assigned to four documentswith no solos. In this case, using the above criteria, both tag A andtag B have the most solos (1), so size is used as a tiebreaker. For someembodiments, the smaller group is used as a tiebreaker, to prevent toomany of the non-solo documents with the tag from being used in othergroups. Thus, in this example, the grouping algorithm of someembodiments would choose tag A for the first group. If there is a tie inboth criteria, some embodiments randomly select one of the co-optimaltags.

Though not shown in these figures, some embodiments may use additionaloptimizations in identifying which tag to select. For instance, someembodiments may prefer tags that are assigned to documents with higherpersonalization scores for the device user, or tags that themselves arepreferred by the user. The personalization scores of some embodimentsare described in further detail in U.S. Provisional Applications62/276,919 and 62/310,751, which are incorporated by reference above.

Once a tag is chosen for a particular iteration, some embodimentsidentify a set of potential groupings of the documents for the tag, witheach potential group including all of the solos for the group (unlessthe number of solos is larger than the maximum number in a group).Again, the process 1600 illustrates one possible means to come up withthese groups, while other techniques may be used in other embodiments.

Specifically, the process 1600 determines (at 1625) whether the selectedtag has more solos than the maximum number of documents allowed in agroup. If this is the case, then the process generates (at 1630) a setof potential groups for the selected tag by determining each possiblepermutation of solos with the max size. For instance, if there are 18solos and the maximum group size is 12 documents, then there are a totalof (18!)/(12!)(6!)=18,564 potential groups. However, as noted below, inthis case all of these possible groups will have the same cost, so onewill be chosen randomly.

If there are fewer than mar solos for the selected tag, the process 1600determines (at 1632) whether the selected tag has fewer than min solos.If this is the case, then the process generates (at 1635) potentialgroups for the tag as (i) all of the solos for the tag plus eachcombination of the remaining documents needed to reach the minimumnumber of documents for a group. For example, the potential groups fortag A in FIG. 17 (for a min value of 3) would all include the one solo,then two of the other four documents assigned tag A (for a total of(4!)/(2!)(2!)=6 potential groups). In other embodiments, all group sizesfrom minimum to maximum are put forth as potential groups, though thismay result in a very large number of potential groups.

If the number of solos is at least min and no greater than max, then theprocess 1600 generates (at 1640) one potential group with all of thesolos. In other embodiments, the process uses all of the solos, as wellas various combinations of all possible group sizes from the number ofsolos up to max. That is, if max=12 and a tag has 8 solos, then the lonegroup of 8 as well as all possible groups of 9, 10, 11, and 12 will becalculated as potential groups.

With the potential groups initially computed, the process 1600 thenaugments (at 1642) each particular potential group with documents thatwould otherwise become an “orphan” (a document that would no longer beassigned any groupable tags) as a result of using that particularpotential group. If there are already the maximum number of documents inthe group, then the group will not be augmented. Furthermore, if thepotential group creates more orphans that are assigned the selected tagthan can be added (because max number of documents will be reached),then some embodiments split this potential group into multiple potentialgroups, by selecting different combinations of the would-be orphans.Other embodiments may not include such an augmentation operation.

Orphans can be created by a potential group in three different ways, insome embodiments. First, any solos for a tag that cannot be includedbecause max is already reached for a potential group will be an orphan.Second, a document might have multiple tags (e.g., A/B), but as a resultof the group selection for one of those tags, the other tag will nolonger be assigned to enough remaining documents to form a group. Theseorphans may be augmented to the potential group at operation 1642. Forexample, if there are three documents with tags A/B and two solos withtag A, then a potential group of the two solos and one of the A/Bdocuments will leave the other two A/B documents as orphans (but theycan be added to this potential group). In addition, some embodimentsaugment groups from previous iterations with these orphans when possible(e.g., so long as those existing groups do not already have maxdocuments).

The third type of orphan is similar, but cannot be added into thepotential group. If a document is not assigned the tag that is beingused to create a potential group, but will not be left with anygroupable tags because other documents with the same tags are pulledinto the group, this document will be an orphan (and cannot be cured byadding it to the potential group). For example, if there are two B/Cdocuments, one A/B document, and two solos with tag A, then a potentialgroup of the one solo and the two A/B documents will leave the two B/Cdocuments as orphans (all examples here assuming a min value of three).Some embodiments will also augment committed groups from previousiterations when possible as well (so long as doing so will not go pastmax documents). In the preceding example, if there was already a groupfor tag C, then the two B/C documents could be added to that group. Thisallows the algorithm of some embodiments to encode each potential groupas (X, Y, Z), where X=the set of documents in the potential group, Y=thewould-be orphans assigned to committed groups, and Z=the set of orphansproduced by the potential group.

Once the potential groups for the iteration are finalized, the process1600 sorts (at 1645) the potential groups based on a short-term orimmediate impact criteria—in this example, the number of orphanscreated. Some embodiments use size as a tiebreaker (e.g., preferringlarger groups, up to max number of documents). In some embodiments, thedevice calculates a score using weighting factors, as with the tagselection at 1620 and 1622. For instance, some embodiments use thenumber of new orphans created by a potential group (e.g., Z in theencoding above) and the size of the potential group as weighted factorsin the calculation. Other factors may be included in some embodiments,such as the personalized relevancy scores of the documents in thepotential group. In some embodiments, these weighting factors aredetermined by running machine learning algorithms on historical userdata. Irrespective of how it is calculated, the immediate impact scoreis a heuristic that can be calculated quickly by the device for eachgroup, and does not involve approximating the rest of the groupassignment process.

FIG. 18 illustrates an example of the computation of potential groupsfor some of the documents 1500. Specifically, the tag A has beenselected, so the dynamic grouping process would be creating potentialgroups from the set of five documents 1800 that are assigned tag A (Docs1, 3, 4, 5, and 8). As shown, numerous potential groups are computed,including four groups 1805-1810 shown here. In this example, potentialgroups of multiple different sizes are created, rather than only groupsof size three (min). Each of the potential groups will include Doc 3,because this is a solo assigned tag A, and then include at least twoadditional documents from the set 1800. In this example, assuming tag Ais the first tag being processed, none of the documents will create anyorphans, and the process selects group 1810 randomly.

Returning to FIG. 16, the process 1600 determines (at 1650) whether thecurrent value for the branching factor k=1. As mentioned, someembodiments initially run the dynamic grouping algorithm by selecting atop potential move using only the short-term impact heuristic, and donot calculate the long-term impact of any of the potential moves.However, if time allows, some embodiments re-run the operations usinglarger branching factors (e.g., k=2, 3, . . . ). Thus, if k=1, theprocess selects (at 1652) the best potential group (ordered according tothe short-term impact heuristic), and commits the documents in thatpotential group as the next group for this current set of groups. Insome embodiments, when the short-term impact scores are equal forseveral groups, the process selects one of the groups randomly (as inFIG. 18). Some embodiments also use the size of a group as a firsttiebreaker (e.g., preferring larger groups, or even groups of specificsizes that have been determined to be most aesthetic).

However, if k>1, the process 1600 selects (at 1655) the k best groupsaccording to the short-term impact heuristic, again selecting randomlyif necessary to break ties. The process computes (at 1660) a long-termimpact evaluation of the group using a heuristic algorithm. For thelonger-term impact heuristic, some embodiments use a greedy algorithmthat approximates the rest of the groupings and identifies a number oforphans that result at the end of this approximation. The process thenselects (at 1662) the potential group (of the k groups evaluated) withthe best long-term heuristic score, and commits the documents in thatpotential group as the next group for this current set of groups. Fortiebreakers, some embodiments use the short-term impact heuristic (e.g.,the new orphans created) and size of group (e.g., preferring largergroups or even groups of specific sizes that have been determined to bemost aesthetic), before selecting randomly among equal potential groups.

Various different algorithms may be used for this long-term impactevaluation. As mentioned, some embodiments use a greedy algorithm thatquickly creates potential groups for various tags. For instance, someembodiments attempt to find groups of each particular allowable sizefrom the remaining documents. As an example, if the allowable group sizeis 3-12, a “bottom-up” algorithm initially identifies any tags that areassigned to exactly 3 documents in the remaining documents, and creategroups of 3 for these tags. Next, with these documents removed, thealgorithm identifies any tags that are assigned to exactly 4 documents,and creates groups of 4 for these tags. This sweep of the documentsproceeds up until any groups of exactly 12 are identified. Because thiswill often leave many groups unidentified (e.g., pulling 4 documentswith tag A out might result in a group of 3 for tag B that was otherwisea group of 5), some embodiments repeat these sweeps until all documentsthat can be grouped have been. The resulting evaluation score, in someembodiments, is the number of orphans left over after the evaluation isperformed.

Some embodiments use more complex scoring based on the long-term impactevaluation. As the evaluation results in a grouping of the documents,some embodiments compute a grouping score as a sum of group scores foreach group within the grouping (set of groups). These group scores, insome embodiments, are computed as a factor of group size and/or documentscores within the group. Each document, in some embodiments, is scoredas a combination of a document relevancy score and potentially otherfeatures. Each of these sub-scores may use weighting factors in someembodiments. In addition, some embodiments explicitly factor in thenumber of orphans, while in other embodiments the score accounts forthese by using the size of each group as a factor in the score (assumingthe same initial number of documents, larger groups on the wholeindicate fewer orphans).

As mentioned, multiple different algorithms that operate in this mannermay be used. For instance, if the algorithm in the previous paragraph(one sweep, starting at min and finishing at max) is referred to asbottom-up (BU) sweep, and the opposite algorithm (one sweep, starting atmax and finishing at min) is referred to as top-down (TD) sweep, thefollowing options are some of the possibilities (it should be understoodthat other possibilities for the long-term evaluation function may beused, either based on these algorithms or using different algorithms):

-   -   TD: one TD sweep    -   BU: one BU sweep    -   TD*: continue repeating TD sweeps until the number of orphans is        not being reduced    -   BU*: continue repeating BU sweeps until the number of orphans is        not being reduced    -   TDBU: one TD sweep followed by one BU sweep    -   BUTD: one BU sweep followed by one TD sweep    -   TDBU*: continue repeating TDBU sweeps until the number of        orphans is not being reduced    -   BUTD*: continue repeating BUTD sweeps until the number of        orphans is not being reduced    -   IO: instead of proceeding from min to max or vice versa,        schedule based on the preferred aesthetic group sizes (e.g.,        start in the middle and work outwards, such as        7/8/6/9/5/10/4/11/3/12, or possibly repeatedly come back to the        preferred sizes (e.g., 7/8/6/9/5/7/8/6/10/4/11/7/8/6/3/12)    -   IO*: continue repeating IO sweeps until the number of orphans is        not being reduced    -   TD*+: continue repeating TD sweeps until the number of orphans        is not being reduced, while each sweep at a particular number of        documents picks up groups with that size and larger sizes (e.g.,        when searching for groups with size 8, groups of sizes        9/10/11/12 would also be identified)    -   BU*+: continue repeating BU sweeps until the number of orphans        is not being reduced, while each sweep at a particular number of        documents picks up groups with that size and smaller sizes        (e.g., when searching for groups with size 8, groups of sizes        7/6/5/4/3 would also be identified)    -   IO*+: continue repeating IO sweeps until the number of orphans        is not being reduced, while each sweep at a particular number of        documents picks up groups with that size and previous sizes

Irrespective of which long-term impact evaluation function is used, anevaluation score (e.g., the number of orphans remaining after performingthe algorithm) is calculated for each of the k potential groups, and oneof the k potential groups is selected (as indicated above, at 1662).Some embodiments also store a hash of each selected move (e.g., a hashof the current state of documents assigned to groups and remainingdocuments), so that future runs through the set of documents will notselect the same state. Next, the process 1600 removes (at 1665) thecommitted documents and the tag used for the group from futurecomputations within this run through the documents (some embodimentswill keep the tag in case the tag has significantly more than mar solos,and create multiple groups for the same tag, however).

The above describes one iteration of a single run of the dynamicgrouping process (i.e., select a tag, identify potential groups for thetag, select a potential group and commit documents for tag). The processthen goes through a series of determinations to identify (i) whether toperform more iterations for the current run, and (ii) if the current runis complete, whether to perform another run (with a larger branchingfactor).

Thus, the process 1600 determines (at 1670) whether additional groupabletags remain after the most recent group has been committed. Assuminggroupable tags remain, then the process returns to 1615, to identify thesize and solo statistics for each remaining tag and select a next tag.However, if there are no tags remaining that are assigned to at leastthe min number of documents, then the process has completed the currentrun.

In this case, the process performs (at 1671) post-processingoptimizations on the set of groups. In some embodiments, the processidentifies any orphans that remain, and attempt to add each of theseorphans to one of the groups in the set of groups (e.g., if this is nothandled at the augmentation operations). Furthermore, some embodimentsdefine an ideal range for group sizes in addition to the explicit min-marange. For groups that are outside this range (e.g., a range of 6-10documents), some embodiments try to swap documents between groups (fordocuments that have more than one tag) so as to produce more groupswithin the ideal size range.

With the set of groups complete, the process 1600 then determines (at1672) whether the current (recently computed) set of groups results infewer orphans than an existing set of groups. If k=1, and this is thefirst time the set of documents has been grouped, then there will not bean existing set of documents yet. However, for subsequent runs, thedocument grouping is compared to the previous best existing run toidentify which should be kept going forward. For instance, if the firstrun (with k=1) returns a grouping that leaves six orphans while thesecond run (with k=2) returns a different grouping that leaves threeorphans, the second grouping will be used. Thus, when the currentgrouping results in fewer orphans than the existing grouping, theprocess uses (at 1675) the current grouping as the set of groups for thefeed, replacing the existing grouping. Otherwise, the process keeps (at1680) the existing grouping.

Some embodiments use more complex scoring to compare sets of groups toeach other. For instance, some embodiments compute a grouping score as asum of group scores for each group within the grouping (set of groups).These group scores, in some embodiments, are computed as a factor ofgroup size and/or document scores within the group. Each document, insome embodiments, is scored as a combination of a document relevancyscore and potentially other features. Each of these sub-scores may useweighting factors in some embodiments. In addition, some embodimentsexplicitly factor in the number of orphans, while in other embodimentsthe score accounts for these by using the size of each group as a factorin the score (assuming the same initial number of documents, largergroups as a whole indicate fewer orphans).

The process 1600 then determines whether to run the iterative groupingoperations again (e.g., with a new value of k). Different embodimentsmay use different conditions than those shown here, as the process 1600presents one example of such a process. As shown, the process determines(at 1685) whether a maximum time has been reached. This time may be ahard cap (e.g., 50 ms from the receipt of the documents) or may be basedon user interaction. For instance, some embodiments continue repeatingthe iterative process until either (i) a grouping that produces noorphans is found (this condition is not shown in process 1600) or (ii)the user has scrolled her feed display past the static content shown atthe top, such that the device is required to display groups. If themaximum time has been reached, the process 1600 ends.

Otherwise, if more time remains, the process determines (at 1690)whether k=k_(max). As mentioned above, some embodiments set a maximumbranching factor so that the process will not run unendingly if the usernever scrolls past the trending topics. As the branching factorincreases, each run through the documents becomes more and moreprocessor-intensive, and some sets of documents may not have a perfect(zero-orphan) solution. Thus, some embodiments set a maximum branchingfactor, and end once this is reached.

However, if k_(max), has not yet been reached, some embodimentsincrement (at 1695) the value of k and remove the group calculations(i.e., remove the commitment of documents to any group, so that theoriginal group remains. The process then returns to 1615 to begin theiterative process again and come up with another grouping of thedocuments.

One aspect of this iterative process is that the selection of documentsfor a tag in a first iteration will affect which tag is selected in thenext iteration (and in subsequent iterations). That is, in some cases,selecting a first group of documents for a first tag results in thedevice selecting a second tag in the next iteration of the process,whereas selecting a second group of documents for the first tag resultsin the device selecting a third tag in the next iteration of the processrather than the second tag. This result occurs because documents may beassigned multiple groupable tags, and assigning a document to a groupfor a first one of its tags means that it will not be assigned to agroup for any of its other tags.

FIGS. 19-21 illustrate this feature for increasing values of k, thebranching factor, representing the group selection as a tree. As shownin FIG. 19, for k=1, the graph can be represented as a tree 1900 withone node at each level. This diagram shows that starting with alldocuments, a first tag (Tag A) is selected. In these diagrams, S1, S2,etc. indicate the potential groups with the best short-term impactheuristics (e.g., number of orphans created, with ties broken randomly).Thus, for Tag A, the top short-term potential group is selected. Thisresults in using Tag B for the next group and again using the topshort-term potential group, which itself results in using Tag C, and soon.

In FIG. 20, the branching factor is set to 2, and thus the graph isrepresented as a tree 2000 with two nodes at each level. In this case,two potential groups S1 and S2 are identified for Tag A, and theevaluation function is applied to both. As shown, this results inevaluation heuristic scores of E2 (for group S1) and E1 (for group S2),with E1 being the best option. As such, a different group of documentsis selected for Tag A in this second case. As a result, rather thanselecting Tag B for the second group, the grouping algorithm selects TagD and identifies the top two potential groups. The process calculatesevaluation heuristic scores for these top two groups, and selects thepotential group with the best score (S1). This process continues untilthe grouping is complete.

FIG. 21 then illustrates a branching factor of 3, with a tree 2100having three nodes at each level. In this case, Tag B is selected forthe first group (e.g., because it ranked equally with Tag A), and theevaluation function is applied to the top 3 groups for Tag B. The bestoption is selected, and Tag A is identified as the second tag. Againthree options are selected and the evaluation heuristic score calculatedfor each of them, with the process again continuing until the groupingis complete. As these examples show, the selection of a particular setof documents for a tag may affect both the tag selected for the nextgroup and the set of documents chosen for that next group.

Once a set of groups is determined, these groups are provided to a feedgenerator in some embodiments. The feed generator determines how tooutput each group into the feed (e.g., identifying a top article for thegroup, determining how to arrange the articles in a group, etc.). Inaddition, some embodiments do not necessarily place the groups in thefeed in the order that the iterative process determines them. Instead,other factors may be used to order the groups within the feed, such asthe relative importance of the topics to the user based on analysis ofthe user's reading history, the size of the groups, the generality orspecificity of the groups, etc.

Several processes that some embodiments use to arrange the groupedsections in the document layout were described above. Several otherprocesses for arranging the grouped sections will now be described byreference to FIGS. 22-28. These processes identify an arrangement of thegrouped sections based on relationships between the groups that theprocesses dynamically derive from topic identifiers that are associatedwith documents that are candidates for the document layout. In otherwords, the arrangement of the grouped sections is not based on statictopic relationships that are predetermined, but rather are defined basedon a dynamic assessment of the topic identifiers of the candidatedocuments. This allows the hierarchal relationships of the groups to bedifferent for different sets of candidate documents, as different setsof topic identifiers might be more prevalent in different sets ofcandidate documents that are identified at different times. Also, thisallows hierarchical relationships to be fluidly defined between groupsas new topic identifiers are dynamically generated or employed (e.g., asnew events occur that are reported by news agencies or blogs).

To illustrate how some embodiments dynamically group documents, assesstheir relationships, and present these groups, FIG. 22 illustrates anoverall process 2200 that a document reader of some embodiments performsto display a document feed page that displays summaries of severaldocuments. This document feeder executes on a computing device (e.g., acomputer or a mobile device) in some embodiments. In some embodiments,the document feeder performs the process 2200 when it starts, when it isbrought to the foreground or it is directed to do so by a user.

As shown, the document reader of some embodiments initially interacts(at 2205) with a set of servers to identify a set of candidate documentsthat are candidates for display by the reader. In some embodiments, theserver set identifies some or all of the candidate documents based onthe document feeds for which the reader is configured (e.g.,automatically or based on user selection). In some embodiments, theprocess receives (at 2205) the candidate documents in order to identifythese documents. In other embodiments, the process identifies (at 2205)the candidate documents from the server without receiving thesedocuments in order to conserve network resources that would be uselesslyconsumed in receiving documents that the reader may never present orsummarize for presentation. In these embodiments, the process identifies(at 2205) the candidate documents by receiving metadata about thesedocuments from the server set. The received metadata for a documentincludes a number of document attributes (e.g., topic tags, feed tags,etc.) in some embodiments. The received metadata for a document alsoincludes in some embodiments one or more components of the document,such as its headline, its headline photo, excerpts and/or RSS feedcontent. The received metadata in some embodiments also includes aserver generated global score for the document.

In some embodiments, each time that the reader performs the process2200, it asks the server set to identify the candidate documents thatare new since the last time that the reader performed the process 2200and identified the last batch of candidate documents. In someembodiments, each time the process asks (at 2205) the server set toidentify a set of candidate documents, the server set identifies all newdocuments associated with all feeds for which the reader is configured,where a new document is one that the server set has not previouslyidentified for the reader.

After 2205, the process 2200 selects (at 2210) a subset of theidentified candidate documents as documents for presenting to the vieweron a feed layout page. The process 2200 selects the subset of documentsbased on viewer's preferences as gauged by the viewer's priorinteractions with the reader (e.g., with the viewer's configuration ofthe reader, with the viewer's interaction with prior documents that thereader presented, etc.). In some embodiments, the reader detects viewerinteractions with one or more displayed documents, and based on theseinteractions, computes attribute scores for attributes associated withthe documents. The computed attribute scores in some embodimentsidentify a preference ranking for attributes associated with thedocuments. The reader in some of these embodiments then use the computedattribute scores to select the subset of candidate documents tosummarize on the feed layout page. This approach is further described inconcurrently filed U.S. patent application Ser. No. 15/273,943, entitled“Document Selection and Display Based on Detected Viewer Preferences”,which is incorporated herein by reference. This concurrently filedpatent application also describes how some embodiments select apersonalized set of documents for presenting on the document summaryfeed page. As described above and below, some embodiments group theselected personalized set of documents into two or more groups andarrange these groups dynamically on the document summary feed page.

Once the process 2200 selects the subset of identified documents todisplay, the process 2200 performs (at 2215) one of the above-describedprocesses to organize the selected subset of documents into two or moregroups of documents. As mentioned above, the reader might not be able toorganize each document in the selected subset into a group. The readerpresents summary panes for such ungrouped documents in an ungroupedsection of the feed layout page. Also, as mentioned above, the feedlayout page in some embodiments includes one or more sections fordisplaying common content that is specified for all viewers.

After organizing the selected subsets of documents that were identifiedparticularly for the viewer into groups, the process then performsoperations 2220 and 2225 (1) to identify dynamic hierarchicalrelationships between the identified groups (i.e., the groups identifiedat 2215) based on the topic identifiers of the set of documentsretrieved at 2205, and (2) to define a presentation order for theidentified groups based on the identified hierarchical relationships andviewer-specific scores for each of the identified groups. These twooperations 2220 and 2225 will be further described below by reference toFIGS. 23 and 24.

Once the process identifies an order for the identified groups at 2225,the process 2200 generates the feed layout page with the sections forthe identified groups arranged based on the identified order. Inaddition to grouped sections that show document summary panes forsimilarly related documents that are collected specifically for aviewer, the feed page layout of some embodiments can also displaysummary panes for common content specified for all viewers and/orsummary panes for documents that could not be grouped with otherdocuments. Hence, in such cases, the order that is identified at 2225specifies just the order of the sections for the identified groups.Other sections (e.g. the common content sections or the ungroupeddocument section) might be placed before, between or after the sectionsfor the identified groups.

FIG. 23 illustrates a process 2300 that the process 2200 performs (at2220 and 2225) to identify the hierarchical relationships between theidentified groups and to define a presentation order for the identifiedgroups based on these relationships. To do these operations, the process2300 dynamically generates a node graph that expresses relationshipsbetween the groups of documents. The process generates this node graphbased on the topic identifiers of the groups as they are used by all theretrieved documents (i.e., by the documents retrieved at 2205). In someembodiments, this graph is a directed acyclic graph in which eachnon-root node only has one parent node and no lower level node is aparent of a higher level node in the graph. The process 2300 will bedescribed by reference to FIG. 24, which pictorially illustratesexamples of several of the operations of the process 2300. Each of theseexamples is illustrated in a different section 2402-2414 of FIG. 24.

As shown, the process 2300 initially starts (at 2305) by defining a nodefor each group of documents identified (at 2215) by the process 2200.Section 2402 of FIG. 24A illustrates fifteen nodes 2400 that are definedfor several document groups identified at 2215. The node that is definedfor a group is assigned the topic identifier of the group (e.g., throughthe node's association with the group).

Next, at 2310, the process 2300 defines a merged node for any set of twoor more nodes that are assigned to two or more topic identifiers thatare associated with the same exact subset of documents in the retrievedset of documents (i.e., in the documents retrieved at 2205). The mergednode replaces the set of nodes for which it was created in the group ofdefined nodes. Section 2404 of FIG. 24A illustrates the group of definednodes after two of these nodes (i.e., nodes 14 and 15) have beenreplaced by a merged node 2460, which is referred to below as thesixteenth node.

The following example illustrates how two nodes that represent twogroups of documents might be merged in some embodiments. Assume that twoarticle groups are defined to contain two sets of articles relating totwo presidential candidates. For these two groups, the process 2300initially defines two nodes at 2305. However, at 2310, the process mightthen determine that each article in the retrieved set of articles (i.e.,each article retrieved at 2205) that is tagged with the name of onepresidential candidate is also tagged with the name of the otherpresidential candidate. Hence, at 2310, the process 2300 creates amerged node for the two nodes previously defined for the two groups ofarticles relating to the two presidential candidates, and has thismerged node replace the previously defined two nodes in the group ofdefined nodes. The merged node replaces the two nodes because for thepurposes of assessing relationships based on the topic identifiers, thetopic-identifier metadata from the retrieved set of documents cannotdifferentiate between these two nodes. Hence, these two nodes shouldjust be treated as a single node. The process 2300 reintroduces thenodes that are replaced by the merged node when it reaches the mergednode as it walks the graph to specify an ordering of the nodes. Whenreintroducing these nodes, the process 2300 places these nodessuccessively in the order in the location that the merged node wouldhave taken in the order.

After 2310, the process 2300 defines (at 2315) directed edges betweenpairs of nodes in the defined group of nodes (i.e., in the group ofnodes initially defined at 2305 that may have been modified at 2310 toinclude merged nodes). Each directed edge identifies one node as theparent node and another node as a child node. The collection of thenodes and edges defines a directed acyclic graph in which each non-rootnode only has one parent node and no lower level node is a parent of ahigher level node in the graph.

Section 2406 of FIG. 24A illustrates an example of such a graph 2465. Asdepicted by graph 2465, the graph defined at 2315 has one root node andseveral non-root nodes. The root node is a placeholder node. The childnodes of the root node (i.e., the nodes in the second level of thegraph) are nodes for which the process 2300 cannot identify another oneof the defined nodes as a parent node. Hence, the process 2300 assignsthe root node as the parent node for these second level nodes, andtreats these second level nodes as a top level topic node.

Each non-root node can have one or more child nodes, but each child nodecan only have one parent node. A non-root node might also not have anychild nodes. Such a childless node is referred to as a leaf node. Asdepicted by graph 2465, the graph defined at 2315 can have differentlength branches (i.e., different leaf nodes can appear at differentdepths of the graph). This is because a first group topic identifiermight have N other group topic identifiers that successively nest underthe first group and each other, while a second group topic identifiermight have M other group topic identifiers that successively nest underthe second group and each other, where N and M are different numbers.For example, in graph 2465, the root child node 4 only has two childnodes 2 and 16 (with node 16 being the merged node), while root childnode 1 has three child nodes 5, 8, and 9, and four grandchild nodes 7,10, 11, and 13.

At 2315, the process 2300 generates the graph by assessing theparent-child relationships between the defined nodes based on real-timedynamic data, which, in turn, allows the nesting and presentation of thegroup sections to be based on real-time data (e.g., data that mirrorsreal-time events in a news cycle). As further described below byreference to FIG. 27, the process 2300 in some embodiments builds theparent-child relationships by using, for the real-time dynamic data, thegroup topic identifiers as they are used by all the retrieved documents.By using the association between the group topic identifiers and theretrieved documents, the process 2300 establishes the parent-childrelationships based on the current usage and prevalence of the topicidentifiers, which can be indicative of current events when thedocuments relate to current events.

Next, at 2320, the process 2300 computes a score for each node in thegraph. Section 2408 of FIG. 24B illustrates the graph 2465 after scoreshave been computed for each node in the graph. In some embodiments, theprocess 2300 computes the score for each leaf node as:Leaf NodeScore=(Average_Personalized_Article_Score+Personalized_Group_Score)/2,where Average_Personalized_Article_Score is the average of thepersonalized scores of the articles in the group represented by the leafnode, and the Group_Score is the personalized score of the group. Thepersonalized group score is based on the viewer personalized score ofthe topic identifier associated with the group. Computing thepersonalized score for a topic identifier and the personalized score foreach article is described in the above-incorporated U.S. patentapplication Ser. No. 15/273,943.

For a non-leaf node, the score also accounts for the scores of theprogeny of the non-leaf node. In some embodiments, the process 2300computes the score for each non-leaf node as:Non-Leaf NodeScore=[Sum_Scores_Progeny_Nodes+(Average_Personalized_Article_Score+Personalized_Group_Score)/2]/(Number_of_Progeny+1),where Sum_Scores_Progeny_Nodes is the sum of the scores of each of thenon-leaf node's progeny nodes, and Number_of_Progeny is the number ofnodes that are the non-leaf node's progeny nodes. This equationessentially averages the non-leaf node's score with the scores of itsprogeny nodes. To compute these scores, the process 2300 starts bycomputing the scores of the leaf nodes and then moves upwards to computethe scores for the rest of the nodes.

After computing (2320) the scores for the nodes in the graph, theprocess then identifies (at 2325) the leaf node with the best score andamplifies the score of this node and each of node that is an ancestor ofthis node. This is done because the process wants to ensure thatoperations 2330-2340 identify the highest scoring leaf node as the firstnode in the node order that the process 2300 produces. Section 2410 ofFIG. 24B illustrates the graph 2465 after the scores along branch 2430have been amplified as the leaf node 2400 a of this branch was thehighest scoring node.

Next, at 2330, the process starts a recursive process in which the rootnode in the graph starts to direct its child nodes to identify asequence of node identifiers of their progeny nodes. Hence, at 2330, theroot node identifies its highest scoring child node and directs it togenerate an ordered sequence of node identifies for its progeny nodesbased on the scores of these nodes. This child node then recursivelydirects its highest scoring child node (assuming that it has one) togenerate an order of its progeny nodes based on their scores. Theserecursive calls are made until the highest scoring leaf node is reached,which returns its own identity. The parent of the highest scoring leafnode then recursively calls its other child nodes (assuming that it hassuch other child nodes).

Once the parent of the highest scoring leaf node has called each of itschild nodes and received their response, it places the node identifierorders that it received from its child nodes in the aggregated orderaccording to the sequence in which it called its child nodes, and thenplaces its own identifier at the end of this aggregated order, and thenreturns this aggregated order. These recursive calls continue (with eachparent node successively calling its child nodes according to theirscores and returning an aggregate ordered sequence after receivingresponses from all its child nodes) until the root node's highestscoring child node returns the ordered list of its progeny nodes.Section 2412 of FIG. 24C illustrates the graph 2465 after the root nodehas received the ordered sequence of node identifiers for the branch2430 that contains the highest scoring leaf node 2400 c.

After receiving the response from its highest scoring child node, theroot node then determines (at 2335) whether it has examined all of itschild nodes. If not, the root node then directs its next highest scoringchild node to generate an ordered sequence of node identifiers for itsprogeny nodes based on the scores of these nodes. The process 2300 loopsthrough 2335 and 2340 until the root node calls each of its child nodesand receives the ordered sequence of node identifiers from each of itschild nodes. When the process determines (at 2335) that it has processedall the child nodes of the root node, it replaces (at 2345) any mergednode's identifier with the identifier of the nodes that the merged nodereplaced, and then ends.

Section 2414 of FIG. 24C illustrates the graph 2465 after the root nodehas received the ordered sequence of node identifiers for all of itschild nodes. This ordered sequence provides the following list of nodeidentifiers: 7, 11, 5, 9, 13, 10, 8, 1, 6, 3, 12, 2, 16 and 4. Node 16is the merged node. Hence, its identifier is replaced by the identifiersof the nodes 14 and 15 that it replaced. After this change, the list ofnode identifiers will be 7, 11, 5, 9, 13, 10, 8, 1, 6, 3, 12, 2, 14, 15and 4. The reader then generates the feed layout page so that thesections for the groups follows this order as indicated by the feedlayout page 2500 of FIG. 25. As mentioned above, the feed layout pagemight include common content or ungrouped content which the readerplaces before, between or after the grouped sections. FIG. 26illustrates an example of a feed layout page 2600 with common contentplaced before and between the grouped sections, and an ungrouped sectionplaced after the grouped sections, where the grouped sections have theirrelative arrangement specified based on the ordered sequence of nodeidentifiers depicted in the section 2414 of FIG. 24C.

FIG. 27 illustrates a process 2700 that the process 2300 of someembodiments uses (at 2315) to construct a graph for a defined set ofgroup nodes by assessing the parent-child relationships between thedefined nodes based on the usage of the group topic identifiers by allthe documents retrieved at 2205. The process 2700 has each node that isdefined for a group at 2305 evaluate other nodes to determine whetherany of them would be suitable as a parent node based on usage of eachnode's topic identifiers by the retrieved documents. Each particularnode evaluates a potential edge to another node as a potential parentnode when at least one retrieved document is associated with the topicidentifier of the particular node and the potential parent node.

The process 2700 initially selects (at 2705) a node for which it has toidentify a parent node. This node in the discussion below is referred toas Node A. Next, the process defines (at 2710) an edge between theselected node A and any other node when at least one of the retrieveddocuments is associated with the topic identifiers of both nodes.Specifically, the process defines a directed edge from node Brepresenting topic B to node A representing topic A when at least oneretrieved document is associated with the tags A and B for topics A andB.

In some embodiments, the process 2700 assigns (at 2710) the followingthree values to an edge between nodes A and B: (1) I, which is thenumber of retrieved documents mentioning topic A but not topic B, (2) J,which is the number of retrieved documents mentioning topic A and topicB, and (3) K, which is the number of retrieved documents mentioningtopic B but not topic A. FIG. 28 illustrates that these 3 valuestogether represent a Venn diagram of all the articles mentioning topic Aor topic B. To ascertain parent-child relationship between the nodes, anedge is only worth considering if J>0, otherwise these two topics sharenothing in common. Hence, no edge is defined (at 2710) when two nodes donot both include at least one article that mentions the grouping topicsof each of the nodes.

For the selected node A, the process might identify (at 2710) severaledges to several potential parent nodes. Hence, for the selected node,the process computes (at 2715) a relatedness value and a specificityvalue for each edge identified for that node. The relatedness value insome embodiments is defined as:

${{RV} = \frac{J}{\left( {I + J + {0.1*\sqrt{K}}} \right)}},$while the specificity value is defined as

${SV} = {\frac{J}{\left( {J + K} \right)}.}$

Because the variable K (the number of articles unique to topic B) isfirst square rooted and then multiplied by a multiplier 0.1, theinfluence of this variable is dampened in the computation of therelatedness value (RV). As such, the relatedness value is largelydefined by the ratio between the number of retrieved articles that areassociated with both topics A and B, and the number of retrievedarticles that are associated with topic A. Conversely, the specificityvalue (SV) expresses the ratio between the number of retrieved articlesthat are associated with both topics A and B, and the number ofretrieved articles that are associated with topic B. Hence, thespecificity value is lower when there are more articles that areassociated with topic B but not topic A. When there are more articlesthat are associated with topic B but not topic A, the relatedness valueis also lower, but this value is influenced more when there are uniquearticles for topic A (i.e., when there are more articles that areassociated with topic A but not topic B).

Based on the relatedness and specificity values, the process 2700 thenselects (at 2720) a parent node for the selected node A. The process2700 is designed in some embodiments to maximize relatedness above acertain value. Hence, it rejects relatedness if it has a value below athreshold value (e.g., 0.7). When one identified particular edge for theselected node A has a relatedness value (RV) greater than the thresholdvalue and greater than the other identified edges, the process selectsthis particular edge as the edge to the parent node B of the selectednode A. However, when the largest relatedness value (above thethreshold) is shared by two or more identified edges, the processselects the edge with the greater specificity value if one of the edgeshas a greater specificity value than the other edges.

If two or more edges with the best relatedness values also haveidentical specificity values, the process uses the personalization scoreof the nodes. If node A has a higher personalization score then node B,the edge is accepted. If nodes A and B happen to have identicalpersonalization score also, the process falls back to comparing themalphabetically (e.g., the edge is accepted when the topic identifier fornode B is alphabetically before the topic identifier for node A, theedge is accepted; otherwise, it is rejected) as its selection must bedeterministic.

After identifying one of the edges as the edge to the parent of selectednode A, the process determines (at 2725) whether it has identified aparent node for all the nodes (i.e., whether it has examined all thenon-root nodes). If not, it selects another node A, and returns to 2710to repeat its operations 2710-2720 for this newly selected node. Whenthe process determines that it has identified a parent node for allnodes, it ends.

As mentioned above, the process 2300 in some embodiments builds theparent-child relationships by using, for the real-time dynamic data, thegroup topic identifiers as they are used by all the retrieved documents.By using the association between the group topic identifiers and theretrieved documents, the process 2300 establishes the parent-childrelationships based on the current usage and prevalence of the topicidentifiers, which can be indicative of current events when thedocuments relate to current events. FIGS. 29 and 30 present two examplesthat illustrate how the process 2300 can dynamically define parent-childrelationships between group topic identifiers differently at differenttimes based on different data that reflects the news events in differentnews cycles.

These two figures illustrate two different node graphs 2900 and 3000that represent two different topic-identifier hierarchies that theprocess 2300 defines for two different news cycles. The node graph 2900of FIG. 29 reflects a news cycle during which the Cleveland Cavalierswin the NBA championship during a year in which the presidentialelection is being held, while the node graph 3000 of FIG. 30 reflects anews cycle during which the Republican National Convention is held inCleveland.

Both of these node graphs 2900 and 3000 have nodes for Cleveland and theCavaliers, but the position of these nodes are nested differently inthese two graphs. Graph 2900 nests the nodes 2950 and 2952 for Clevelandand the Cavaliers under the NBA node 2954, which it nests under theSports node 2958 along with the Browns node 2956. On the other hand,graph 3000 nests the Cleveland node 3050 under the Republican Candidatenode 3054 along with the RNC node 3052. In this graph, the RepublicanCandidate node 3054 and the Democratic Candidate node 3056 are childnodes of the Presidential Election node 3058, which is a child node ofthe Politics node 3064 along with the EU node 3060 and the Brexit node3062. In graph 2900 of FIG. 29, the Presidential Election node 2960 alsoexists, but neither it nor any of its progeny nodes 2962 and 2964 are aparent of the Cleveland node 2950. Also, while the nodes 2950 and 2952for Cleveland and Cavaliers are sibling child nodes of the Sports node,these two nodes 3050 and 3066 are not sibling nodes in the node graph3000. This is because Cleveland node 2950 is part of the Sports branchof the graph 2900 of FIG. 29, while the Cleveland node 3050 is part ofthe Politics branch of the graph 3000 of FIG. 30.

FIGS. 29 and 30 also illustrate that their different node graphs 2900and 3000 result in different layouts of the article summary pages 2910and 3010. In the article layout 2900, the Cleveland section appears in agroup of the Sports related sections (i.e., in the group of sectionsthat include sections for the Cavaliers, Cleveland, NBA, Browns,Sports). On the other hand, in the article layout 3000, the Clevelandsection appears in a group of the Politics related sections (i.e., inthe group of sections that include sections for the RNC, Cleveland,Republican Candidate, Democratic Candidate, Presidential Election, EU,Brexit, Politics). Also, while the sections for Cleveland and Cavaliersappears next to each other in the article summary page 2910, thesections for these two topics do not appear next to each other in thearticle summary page 3010.

III. Electronic System

Many of the above-described features and applications are implemented assoftware processes that are specified as a set of instructions recordedon a computer readable storage medium (also referred to as computerreadable medium). When these instructions are executed by one or morecomputational or processing unit(s) (e.g., one or more processors, coresof processors, or other processing units), they cause the processingunit(s) to perform the actions indicated in the instructions. Examplesof computer readable media include, but are not limited to, CD-ROMs,flash drives, random access memory (RAM) chips, hard drives, erasableprogrammable read-only memories (EPROMs), electrically erasableprogrammable read-only memories (EEPROMs), etc. The computer readablemedia does not include carrier waves and electronic signals passingwirelessly or over wired connections.

In this specification, the term “software” is meant to include firmwareresiding in read-only memory or applications stored in magnetic storagewhich can be read into memory for processing by a processor. Also, insome embodiments, multiple software inventions can be implemented assub-parts of a larger program while remaining distinct softwareinventions. In some embodiments, multiple software inventions can alsobe implemented as separate programs. Finally, any combination ofseparate programs that together implement a software invention describedhere is within the scope of the invention. In some embodiments, thesoftware programs, when installed to operate on one or more electronicsystems, define one or more specific machine implementations thatexecute and perform the operations of the software programs.

A. Mobile Device

The user data sharing of some embodiments occurs on mobile devices, suchas smart phones (e.g., iPhones®) and tablets (e.g., iPads®). FIG. 31 isan example of an architecture 3100 of such a mobile computing device. Asshown, the mobile computing device 3100 includes one or more processingunits 3105, a memory interface 3110 and a peripherals interface 3115.

The peripherals interface 3115 is coupled to various sensors andsubsystems, including a camera subsystem 3120, a wired communicationsubsystem(s) 3123, a wireless communication subsystem(s) 3125, an audiosubsystem 3130, an I/O subsystem 3135, etc. The peripherals interface3115 enables communication between the processing units 3105 and variousperipherals. For example, an orientation sensor 3145 (e.g., a gyroscope)and an acceleration sensor 3150 (e.g., an accelerometer) is coupled tothe peripherals interface 3115 to facilitate orientation andacceleration functions.

The camera subsystem 3120 is coupled to one or more optical sensors 3140(e.g., a charged coupled device (CCD) optical sensor, a complementarymetal-oxide-semiconductor (CMOS) optical sensor, etc.). The camerasubsystem 3120 coupled with the optical sensors 3140 facilitates camerafunctions, such as image and/or video data capturing. The wiredcommunication subsystem 3123 and wireless communication subsystem 3125serve to facilitate communication functions.

In some embodiments, the wireless communication subsystem 3125 includesradio frequency receivers and transmitters, and optical receivers andtransmitters (not shown in FIG. 31). These receivers and transmitters ofsome embodiments are implemented to operate over one or morecommunication networks such as a GSM network, a Wi-Fi network, aBluetooth network, etc. The audio subsystem 3130 is coupled to a speakerto output audio (e.g., to output voice navigation instructions).Additionally, the audio subsystem 3130 is coupled to a microphone tofacilitate voice-enabled functions in some embodiments.

The I/O subsystem 3135 involves the transfer between input/outputperipheral devices, such as a display, a touch screen, etc., and thedata bus of the processing units 3105 through the peripherals interface3115. The I/O subsystem 3135 includes a touch-screen controller 3155 andother input controllers 3160 to facilitate the transfer betweeninput/output peripheral devices and the data bus of the processing units3105. As shown, the touch-screen controller 3155 is coupled to a touchscreen 3165. The touch-screen controller 3155 detects contact andmovement on the touch screen 3165 using any of multiple touchsensitivity technologies. The other input controllers 3160 are coupledto other input/control devices, such as one or more buttons. Someembodiments include a near-touch sensitive screen and a correspondingcontroller that can detect near-touch interactions instead of or inaddition to touch interactions.

The memory interface 3110 is coupled to memory 3170. In someembodiments, the memory 3170 includes volatile memory (e.g., high-speedrandom access memory), non-volatile memory (e.g., flash memory), acombination of volatile and non-volatile memory, and/or any other typeof memory. As illustrated in FIG. 31, the memory 3170 stores anoperating system (OS) 3171. The OS 3171 includes instructions forhandling basic system services and for performing hardware dependenttasks. The memory 3170 additionally includes layout rearranginginstructions 3172 in order for the device 3100 to perform the layoutrearranging process of some embodiments. In some embodiments, theseinstructions 3172 may be a subset of the operating system instructions3171, or may be part of the instructions for an application.

The memory 3170 also includes communication instructions 3174 tofacilitate communicating with one or more additional devices (e.g., forpeer-to-peer data sharing, or to connect to a server through theInternet for cloud-based data sharing); graphical user interfaceinstructions 3176 to facilitate graphic user interface processing; imageprocessing instructions 3178 to facilitate image-related processing andfunctions; input processing instructions 3180 to facilitateinput-related (e.g., touch input) processes and functions; audioprocessing instructions 3182 to facilitate audio-related processes andfunctions; and camera instructions 3184 to facilitate camera-relatedprocesses and functions. The instructions described above are merelyexemplary and the memory 3170 includes additional and/or otherinstructions in some embodiments. For instance, the memory for asmartphone may include phone instructions to facilitate phone-relatedprocesses and functions. The above-identified instructions need not beimplemented as separate software programs or modules. Various functionsof the mobile computing device can be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits.

While the components illustrated in FIG. 31 are shown as separatecomponents, one of ordinary skill in the art will recognize that two ormore components may be integrated into one or more integrated circuits.In addition, two or more components may be coupled together by one ormore communication buses or signal lines. Also, while many of thefunctions have been described as being performed by one component, oneof ordinary skill in the art will realize that the functions describedwith respect to FIG. 31 may be split into two or more integratedcircuits.

B. Computer System

FIG. 32 conceptually illustrates another example of an electronic system3200 with which some embodiments of the invention are implemented. Theelectronic system 3200 may be a computer (e.g., a desktop computer,personal computer, tablet computer, etc.), phone, PDA, or any other sortof electronic or computing device. Such an electronic system includesvarious types of computer readable media and interfaces for variousother types of computer readable media. Electronic system 3200 includesa bus 3205, processing unit(s) 3210, a graphics processing unit (GPU)3215, a system memory 3220, a network 3225, a read-only memory 3230, apermanent storage device 3235, input devices 3240, and output devices3245.

The bus 3205 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of theelectronic system 3200. For instance, the bus 3205 communicativelyconnects the processing unit(s) 3210 with the read-only memory 3230, theGPU 3215, the system memory 3220, and the permanent storage device 3235.

From these various memory units, the processing unit(s) 3210 retrievesinstructions to execute and data to process in order to execute theprocesses of the invention. The processing unit(s) may be a singleprocessor or a multi-core processor in different embodiments. Someinstructions are passed to and executed by the GPU 3215. The GPU 3215can offload various computations or complement the image processingprovided by the processing unit(s) 3210. In some embodiments, suchfunctionality can be provided using CoreImage's kernel shading language.

The read-only-memory (ROM) 3230 stores static data and instructions thatare needed by the processing unit(s) 3210 and other modules of theelectronic system. The permanent storage device 3235, on the other hand,is a read-and-write memory device. This device is a non-volatile memoryunit that stores instructions and data even when the electronic system3200 is off. Some embodiments of the invention use a mass-storage device(such as a magnetic or optical disk and its corresponding disk drive,integrated flash memory) as the permanent storage device 3235.

Other embodiments use a removable storage device (such as a floppy disk,flash memory device, etc., and its corresponding drive) as the permanentstorage device. Like the permanent storage device 3235, the systemmemory 3220 is a read-and-write memory device. However, unlike storagedevice 3235, the system memory 3220 is a volatile read-and-write memory,such a random access memory. The system memory 3220 stores some of theinstructions and data that the processor needs at runtime. In someembodiments, the invention's processes are stored in the system memory3220, the permanent storage device 3235, and/or the read-only memory3230. For example, the various memory units include instructions forprocessing multimedia clips in accordance with some embodiments. Fromthese various memory units, the processing unit(s) 3210 retrievesinstructions to execute and data to process in order to execute theprocesses of some embodiments.

The bus 3205 also connects to the input and output devices 3240 and3245. The input devices 3240 enable the user to communicate informationand select commands to the electronic system. The input devices 3240include alphanumeric keyboards and pointing devices (also called “cursorcontrol devices”), cameras (e.g., webcams), microphones or similardevices for receiving voice commands, etc. The output devices 3245display images generated by the electronic system or otherwise outputdata. The output devices 3245 include printers and display devices, suchas cathode ray tubes (CRT) or liquid crystal displays (LCD), as well asspeakers or similar audio output devices. Some embodiments includedevices such as a touchscreen that function as both input and outputdevices.

Finally, as shown in FIG. 32, bus 3205 also couples electronic system3200 to a network 3225 through a network adapter (not shown). In thismanner, the computer can be a part of a network of computers (such as alocal area network (“LAN”), a wide area network (“WAN”), or anIntranet), or a network of networks, such as the Internet. Any or allcomponents of electronic system 3200 may be used in conjunction with theinvention.

Some embodiments include electronic components, such as microprocessors,storage and memory that store computer program instructions in amachine-readable or computer-readable medium (alternatively referred toas computer-readable storage media, machine-readable media, ormachine-readable storage media). Some examples of such computer-readablemedia include RAM, ROM, read-only compact discs (CD-ROM), recordablecompact discs (CD-R), rewritable compact discs (CD-RW), read-onlydigital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a varietyof recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.),flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.),magnetic and/or solid state hard drives, read-only and recordableBlu-Ray® discs, ultra density optical discs, any other optical ormagnetic media, and floppy disks. The computer-readable media may storea computer program that is executable by at least one processing unitand includes sets of instructions for performing various operations.Examples of computer programs or computer code include machine code,such as is produced by a compiler, and files including higher-level codethat are executed by a computer, an electronic component, or amicroprocessor using an interpreter.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, some embodiments areperformed by one or more integrated circuits, such as applicationspecific integrated circuits (ASICs) or field programmable gate arrays(FPGAs). In some embodiments, such integrated circuits executeinstructions that are stored on the circuit itself. In addition, someembodiments execute software stored in programmable logic devices(PLDs), ROM, or RAM devices.

As used in this specification and any claims of this application, theterms “computer”, “server”, “processor”, and “memory” all refer toelectronic or other technological devices. These terms exclude people orgroups of people. For the purposes of the specification, the termsdisplay or displaying means displaying on an electronic device. As usedin this specification and any claims of this application, the terms“computer readable medium,” “computer readable media,” and “machinereadable medium” are entirely restricted to tangible, physical objectsthat store information in a form that is readable by a computer. Theseterms exclude any wireless signals, wired download signals, and anyother ephemeral signals.

While the invention has been described with reference to numerousspecific details, one of ordinary skill in the art will recognize thatthe invention can be embodied in other specific forms without departingfrom the spirit of the invention. For instance, a number of the figures(including FIGS. 7, 8, 14, 16, 22, 23, and 27) conceptually illustrateprocesses. The specific operations of these processes may not beperformed in the exact order shown and described. The specificoperations may not be performed in one continuous series of operations,and different specific operations may be performed in differentembodiments. Furthermore, the process could be implemented using severalsub-processes, or as part of a larger macro process. Thus, one ofordinary skill in the art would understand that the invention is not tobe limited by the foregoing illustrative details, but rather is to bedefined by the appended claims.

We claim:
 1. A method for displaying documents on a device, the methodcomprising: at the device: receiving a plurality of documents, without apresentation grouping, from one or more servers, each document assignedone or more tags indicating relevancy to a topic; identifying, from theone or more tags, one or more groupable tags that define potentialgroups to assign the plurality of documents to, the one or moregroupable tags comprising a subset of a set of the one or more groupabletags that are associated with a minimum groupable number of theplurality of documents; identifying presentation groups from thepotential groups based upon characteristics of the plurality ofdocuments, by, iteratively: for each potential group of the potentialgroups, identifying a number of ungrouped documents that would exist ifthe potential group were selected as a presentation group, the ungroupeddocuments comprising a subset of the plurality of documents that wouldnot group with any potential groups; identifying a top group of thepotential groups having a fewest number of ungrouped documents;selecting the top group of the potential groups as a presentation groupof the presentation groups; and dynamically assigning a correspondingsubset of the plurality of documents to the presentation group of thepresentation groups, removing the corresponding subset of the pluralityof documents for assignment in subsequent iterations; and displaying atleast a portion of the plurality of documents on the device arrangedaccording to their assigned presentation groups.
 2. The method of claim1, wherein the plurality of documents received from the one or moreservers are based on preferences of a user of the device.
 3. The methodof claim 1, wherein identifying the one or more groupable tags comprisesidentifying particular tags of the one more tags that match a set oftags indicated in a whitelist, wherein the set of tags on the whitelistare designated as high-level topic tags.
 4. The method of claim 3,wherein the set of tags on the whitelist are received from the one ormore servers.
 5. The method of claim 1, comprising identifying a firstpresentation group of the presentation groups for a first groupable tagof the one or more groupable tags by selecting a first potential groupfrom a plurality of potential groups for the first groupable tag basedon a move-order heuristic that minimizes a number documents of theplurality of documents associated with the first groupable tag that willnot be assigned to a presentation group if a potential group of theplurality of potential groups is selected as the first presentationgroup.
 6. The method of claim 1, wherein receiving the plurality ofdocuments comprises: selecting a first plurality of documents to beincluded in the potential groups, wherein the first plurality ofdocuments comprises a predefined percentage of the plurality ofdocuments.
 7. The method of claim 1, wherein a set of documents assignedto a first potential group affects the one or more groupable tagsselected for a second potential group.
 8. The method of claim 1,comprising assigning the plurality of documents to the potential groupsby: selecting a first groupable tag of the one or more groupable tagsfor a first potential group based on a first criteria; and assigning afirst subset of the plurality of documents, to which the first groupabletag is assigned, to the first potential group based on a second set ofcriteria.
 9. The method of claim 1, comprising: selecting a firstgroupable tag of the one or more groupable tags according to a first setof grouping criteria; assigning documents of the plurality of documentshaving the first selected tag to a first set of one or more potentialgroups of documents; selecting a first potential group of the first setof potential groups according to a second set of grouping criteria;assigning documents of the first selected potential group to a firstpresentation group; removing the documents in the selected potentialgroup of documents and the first groupable tag from future considerationfor being included in future potential groups of the plurality ofdocuments; selecting a second groupable tag from one or more tagsaccording to the first set of grouping criteria; assigning documents ofthe plurality of documents having the second selected groupable tag to asecond set of one or more potential groups of documents; selecting asecond potential group of the set of second potential groups accordingto the second set of grouping criteria; and assigning documents of thesecond selected potential group to a second presentation group.
 10. Themethod of claim 9, wherein: the first set of grouping criteria comprisesa first number of documents having a selected groupable tag and a secondnumber of documents for which a selected groupable tag is the only tag adocument has that can be used to form a potential group; and the secondset of grouping criteria comprises a third number of documents having aselected groupable tag that will not be included in a presentationgroup.
 11. A non-transitory machine readable medium storing a programwhich when executed by a set of processing units of a device displaysdocuments on the device, the program comprising sets of instructionsfor: receiving a plurality of documents, without a presentationgrouping, from one or more servers, each document assigned one or moretags indicating relevancy to a topic; identifying, from the one or moretags, one or more groupable tags that define potential groups to assignthe plurality of documents to, the one or more groupable tags comprisinga subset of a set of the one or more groupable tags that are associatedwith a minimum groupable number of the plurality of documents;identifying presentation groups from the potential groups based uponcharacteristics of the plurality of documents, by, iteratively: for eachpotential group of the potential groups, identifying a number ofungrouped documents that would exist if the potential group wereselected as a presentation group, the ungrouped documents comprising asubset of the plurality of documents that would not group with anypotential groups; identifying a top group of the potential groups havinga fewest number of ungrouped documents; selecting the top group of thepotential groups as a presentation group of the presentation groups; anddynamically assigning a corresponding subset of the plurality ofdocuments to the presentation group of the presentation groups, removingthe corresponding subset of the plurality of documents for assignment insubsequent iterations; and displaying at least a portion of theplurality of documents on the device arranged according to theirassigned presentation groups.
 12. The non-transitory machine readablemedium of claim 11, wherein the set of instructions for dynamicallyassigning the plurality of documents to the presentation groupscomprises sets of instructions for: computing a first assignment of theplurality of documents to a first set of the potential groups;determining whether a display has been scrolled to view a first set ofthe presentation groups of documents; and when a user of the device hasnot yet scrolled the display to view the first set of the presentationgroups, computing a second assignment of the plurality of documents to asecond set of the potential groups.
 13. The non-transitory machinereadable medium of claim 12, wherein the set of instructions fordisplaying the documents arranged according to the presentation groupscomprises a set of instructions for determining whether to includedocuments of the first set of the potential groups or documents of thesecond set of the potential groups in the first set of the presentationgroups according to a set of grouping criteria, wherein the groupingcriteria comprises a maximum number of documents that will be part of apotential group when its groupable tag is selected as the tag of the onemore groupable tags.
 14. The non-transitory machine readable medium ofclaim 13, wherein the set of grouping criteria measures a number of thedocuments that are not assigned to any of the potential groups.
 15. Thenon-transitory machine readable medium of claim 12, wherein, when thefirst assignment of the plurality of documents to the first set ofpotential groups and the second assignment of the plurality of documentsto the second set of the potential groups use varying amounts of time,and the second assignment of the plurality of documents to the secondset of the potential groups uses more time to explore more possiblegroups for each of a set of the groupable tags than the first assignmentof the plurality of documents to the first set of the potential groups.16. The non-transitory machine readable medium of claim 12, wherein theset of instructions for displaying the documents comprises a set ofinstructions for displaying a set of content designated as curatedcontent above the grouped documents, wherein the set of instructions fordetermining whether the user has scrolled to view the first set of thepresentation groups comprises a set of instructions for determiningwhether the user has scrolled past the curated content.
 17. Thenon-transitory machine readable medium of claim 16, wherein the set ofcurated content comprises a set of top documents displayed in a displayarea designated for the set of top documents that are sent from the oneor more servers to a plurality of devices of different users.
 18. Thenon-transitory machine readable medium of claim 11, wherein a subset ofthe plurality of documents are not assigned to any of the presentationgroups.
 19. The non-transitory machine readable medium of claim 18,wherein the program further comprises a set of instructions fordisplaying the subset of the documents not assigned to any of thepresentation groups after a final presentation group of the assignedpresentation groups is displayed.
 20. The non-transitory machinereadable medium of claim 11, wherein the program further comprises a setof instructions for identifying a first set of potential groups for afirst groupable tag of the one or more groupable tags based on abranching factor indicative of a number of potential groups to beincluded in the first set of potential groups.